Condition monitoring and prognosis of utility scale wind turbines

REVIEW

Condition monitoring and prognosis of utility scale wind turbines R. W. Hyers1, J. G. McGowan1, K. L. Sullivan1, J. F. Manwell1 and B. C. Syrett2 The state of the art in condition monitoring in wind turbines, and related technologies currently applied in practice and under development for aerospace applications, are reviewed. Condition monitoring systems estimate the current condition of a machine from sensor measurements, whereas prognosis systems give a probabilistic forecast of the future condition of the machine under the projected usage conditions. Current condition monitoring practice in wind turbine rotors involves tracking rotor imbalance, aerodynamic asymmetry, surface roughness and overall performance and offline and online measurements of stress and strain. Related technologies for monitoring of load history and fatigue crack growth in aircraft structures are evaluated for their applicability to wind turbine blades. Similarly, condition monitoring practice in wind turbines is compared with monitoring and prognosis in helicopter gearboxes. The state of the art in condition monitoring of electronic controls, power electronics and towers is also evaluated and compared with the state of the art in aerospace. Based on these comparisons, technology needs and future challenges for the development of condition monitoring and prognosis for large wind machines, both onshore and offshore, are summarised. Keywords: Wind energy, Condition monitoring, Prognosis

Introduction Wind energy is a commercial technology. Research in this area must increase the profitability of this technology, or the new results will not be adopted by commercial users. Technologies that monitor the condition of the turbine and changes in this condition over time currently contribute in a few small ways to this objective. However, to be useful, condition monitoring technologies must contribute more than the alternatives (such as run to failure, or periodic manual inspection). Areas where current condition monitoring technologies can contribute more than the alternatives include reduced maintenance cost, increased lifetime of components, increased reliability, improved safety and decreased downtime. More sophisticated technologies offer great promise for reducing costs and increasing the reliability of the largest onshore wind turbines, and even greater potential for offshore turbines. A condition monitoring system must produce actionable information to be useful. That is, the indications of fault must be sufficiently specific and credible that the operator will order the maintenance action requested by the condition monitoring system based on its recommendation alone. The challenge is in raising the fraction of faults detected, detecting these faults as early as possible and correctly identifying the faulty component, 1 2

University of Massachusetts, Amherst, MA 01003, USA Electric Power Research Institute, Palo Alto, CA 94304, USA

*Corresponding author, email [email protected]

ß 2006 Institute of Materials, Minerals and Mining and W. S. Maney & Son Ltd Received 23 October 2006; accepted 9 November 2006 DOI 10.1179/174892406X163397

all while reducing false positive indications to an acceptable level.

Historical approach to condition monitoring The historical approach to maintenance in wind turbines is to run them to failure, with only limited periodic replacement of wear items like oil and filters. As wind machines have grown in capacity and become more expensive, run to failure has become less practical as a strategy. Many operators employ periodic inspections by skilled technicians, who use their senses, and occasionally portable analysis tools, to assess the condition of the turbine during scheduled maintenance. Indeed, many operators assert that these inspections alone are sufficient for maintenance of the turbines,1 especially when combined with simple online monitoring of the gearbox oil (see section ‘Condition monitoring in gearboxes and generators’). These inspections constitute a lower limit for performance of an automated condition monitoring system. Any condition monitoring system must provide an economic advantage over periodic inspections, not only a technical advantage. ‘Offline’ condition monitoring technologies are machine aided periodic inspections, which require that the machine be shut down, and/or require the attention of an operator. In 1994, when Sutherland et al.2 surveyed this topic, condition monitoring in wind turbines was almost exclusively offline. For example, modal analysis of blades was employed extensively, using accelerometers and/or laser Doppler techniques3 to validate finite element simulations of the elastic

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1 Cost of wind turbine components:9 cost risen from y15% of total system cost to bines have become larger, while cost (transmission and generator) has held y30%

of rotor has .25% as turof drivetrain constant at

deformations. These offline techniques are well suited to design and certification of new classes of wind machines. However, they are not suitable for determining the present condition of an individual wind machine, much less for projecting its future condition. ‘Online’ condition monitoring technologies monitor the machine continuously during operation. These online technologies may report continuous raw measurements (strain, vibration, etc.), or may incorporate onboard processing for data reduction and analysis. Online monitoring of fluids such as gearbox oil usually involves sampling a small fraction of the circulating fluid; however, a third class of measurements, ‘inline’, is a form of online monitoring of a fluid in which all of the circulating fluid is monitored. With current condition monitoring technologies for onshore turbines, the economic advantage of all but the simplest online condition monitoring technologies is uncertain.1 However, condition monitoring will be increasingly important in larger turbines owing to the greater cost of the components and greater concern about their reliability. Furthermore, condition monitoring will be an essential technology for offshore turbines, owing to their projected size, limited accessibility and consequent need for greater reliability.

Current practice in condition monitoring of wind turbines Condition monitoring is particularly beneficial for the gearbox and generator and the rotors and blades, as these subsystems dominate the total machine cost in turbines .1.5 MW (Fig. 1); the gearbox and generator also comprise the least reliable turbine system (Fig. 2). A great deal of research on monitoring the condition of a wide variety of mechanical components has been conducted, and a wide variety of commercial systems are offered for monitoring various components in different industries. For the specific application of wind power, commercial availability of systems for monitoring the gearbox and bearings is the most advanced. Many of these systems are generic and are simply applied to monitoring the bearings in wind turbines; however several small companies and joint ventures specialise in monitoring the condition of wind turbine mechanicals. Some are listed in references.4–8 The drivetrain is not only the most valuable subsystem in wind turbines (see Fig. 1),9 but also one of the most

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2 Causes of failures of wind turbines:8 gearbox and generator, totalling y30% of cost and responsible for 17% of failures, are clearly leading candidates for condition monitoring

trouble prone (see Fig. 2), with gearbox and generator failures accounting for 17% of all failures reported in a study of Danish turbines in 2001.8 For these reasons, some protection of the gearbox and generator is mandated by insurers in the German market.10 Development of condition monitoring for rotors is a current topic of research and development. As shown in Fig. 1, the cost of rotors is the only cost that is increasing as a fraction of the total cost as turbine size increases, and now almost equals the sum of all nacelle components (drivetrain, gearbox and power electronics). While blades accounted for only y5% of the failures in the Danish study of 2001 (Fig. 2), the population of turbines in that study included very few turbines .1 MW.8 As the blades continue to increase in cost and mass with the introduction of ever larger wind machines, there is a great deal of concern about their reliability. One report states ‘built-in monitoring systems will be a key enabler for future megaturbines’.11

Objectives of condition monitoring Condition monitoring has the potential to have a dramatic impact in the future as turbines become larger and offshore turbines become more common. The major impacts of condition monitoring may be summarised as follows.

Condition based maintenance This current and near term approach involves repair or replacement of parts based on their actual condition and the individual operating history of the particular machine, rather than on a schedule based on predicted operating conditions of the average machine. Condition monitoring allows the operator to avoid replacing good parts solely because of age. The German insurers require an extensive overhaul of wind turbines every 5 years or 40 000 operating hours, unless a certified condition monitoring system is installed.10 These certified systems monitor the vibration of the gearbox, bearings and generators to detect damage.

Fault containment Also this is a current and near term strategy to prevent the propagation of faults from the component level to

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the subsystem level. For example, failure of a $1500 bearing could result in a $100 000 gearbox replacement, a $50 000 generator rewind and another $70 000 in expenses to replace the failed components.12,13 Combined with the lost production owing to the major unexpected repair, the total cost approaches $250 000. In contrast, Rademakers14 estimates the cost for the unscheduled replacement of the bearing alone as J16 500, y$20 000, including generous allowances for downtime, labour and access.

Remote diagnosis This is a near term strategy. For wind turbines, the cost of downtime drives the need for condition monitoring. For a current onshore turbine of 1.7 MW, running at 25% of capacity, and selling electricity at $0.10/kWh (including all green credits, tax credits, etc.), the cost of downtime is $1020/day. For a hypothetical near term offshore turbine of 12 MW running at 35% capacity, this cost rises to $10 080. However, for much of the year, access to the offshore turbine is restricted by weather, so the outage may exceed 90 days for a 2 day repair, at a cost of .$900 000. Clearly there is great value in avoiding any unscheduled downtime in the latter case. Second, since access is severely limited by wind, weather and waves, outages for unscheduled maintenance will be longer than that for onshore turbines, and having the correct part on the repair boat during the first visit is critical to a timely repair.

Prognosis A future approach that combines information on each machine’s current condition with historical data from machines of the same class, models of the physics of failure of components and short term projected usage to predict the future probability of failure of that individual machine. That is, prognosis gives a probabilistic forecast that is specific to each machine, allowing a strategy that balances the risk of running a machine with damage indications against the lost revenue while waiting for maintenance. Prognosis has the largest potential payoff of all condition monitoring technologies, especially for offshore turbines. Projects sponsored by the European Union are currently driving advances in condition monitoring for wind power. For example, Condition Monitoring for Offshore Wind Farms (CONMOW, 2002–2006, J2m)8,15,16 is a collaboration among researchers from the Energy Research Centre of the Netherlands, academics at Loughborough University in the UK and industry in the Netherlands, Denmark, UK and Germany. Condition Monitoring for Offshore Wind Farms is focused on identifying and advancing current systems and identifying gaps in technology for applying condition monitoring to offshore wind turbines. It is the successor to Wind Turbine Operation and Maintenance based on Condition Monitoring (WT-OMEGA, 1999– 2003),7 which attempted to evaluate different condition monitoring technologies on a research turbine. Other large European research projects in this area include: Offshore M&R (2003–2005),17,18 CleverFarm (2000– 2003, J800k),15,19 OPTIMAT (2002–2007, J4.5m)15,20 and SIMU-Wind.21 However, research that is beneficial to wind turbines is not confined to Europe, with the US being a significant contributor to health and usage monitoring systems (HUMS) for helicopters (see section

Condition monitoring and prognosis of utility scale wind turbines

‘Related technologies: HUMS and EMS’) and leading efforts in prognosis of airplanes and spacecraft (see section ‘Damage prognosis’).

Related technologies Damage prognosis For parts without condition monitoring, service limits will be set based on a priori assumptions about the service conditions and initial condition of the parts, with replacement required when some small fraction (0.1% is typical) is predicted to fail. Condition monitoring provides more information on the current state of a component. Once a change in condition is detected, an operator is alerted. For severe degradations, automatic shutdowns may occur to limit secondary damage. In the case of an alert, the operator has many options, from ignoring the fault indication to watching the evolution of the damage signature, ordering inspections or maintenance or ordering preemptive shutdown. Condition monitoring alone provides little or no guidance to help the operator decide among these and other options. The basis of prognosis is that failure is a process, not an event. The earlier in the process it is detected, the greater is the flexibility that exists for managing the process of degradation and failure of a component and its higher level systems (see Fig. 3). Prognosis allows a change in approach to managing assets. Instead of time based replacements, maintenance is determined by the individual and actual remaining performance.23 Uncertainty is managed by reliable, physics based predictive capability combined with condition monitoring. Furthermore, a reliable indication of damage may be merely a data point in the updated estimate of current condition, rather than a cause for alarm or immediate repairs, depending on the resulting forecast of future capability and future usage. Condition monitoring is one input to the prognosis process, but other inputs are combined with the current state of the component in developing the forecast (Fig. 4). Each piece of new information reduces the uncertainty in the updated forecast (Bayesian updating) (Fig. 5). All this information is combined with a short range forecast of the operating environment to provide an updated estimate of the remaining life of the component. A prognosis system not only provides decreased risk of catastrophic failure in severe service, but also allows recovery of residual performance owing to operating conditions less severe than the design limits (Fig. 6). Seeliger et al.21 have been studying the use of condition monitoring together with physics based simulation models to assist in the prognosis of wind turbine failures. In this paper they discuss the use of a multibody mechanical simulation model, an electrical simulation model and a comprehensive multisensor conditioning monitoring system. The eventual output of this research is intended to be the capability to predict the nature and timing of failures well before they happen. Prognosis is currently implemented in the Joint Strike Fighter (JSF) and in HUMS for both civil and military helicopters, and part of this experience is relevant to wind turbines. Prognosis is an active area of research,

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3 Evolution of mechanical failure22

with significant programmes being sponsored by DARPA, the US Air Force, US Navy and NASA, as well as significant research within the aerospace industry. The value of prognosis is particularly valuable for offshore turbines. With a good prognosis system, it will be possible to make an accurate estimate about whether or not a damaged component will survive the season

under normal operating conditions. Furthermore, it will be possible to determine revised operating limits that will maximise the energy produced until shutdown is required. For example, the maximum wind speed for a damaged turbine could be reduced, but that machine could still operate normally on all but the windiest days. Early efforts in applying prognosis to wind machines are described by Seeliger et al.21

4 Prognosis versus condition monitoring:24 condition monitoring consists of only interrogation and state awareness; prognosis adds historical databases and models of physics of failure and projected operating conditions to predict condition of component at future time

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5 Additional information gained over time X allows updated projection (grey) of probability of failure at future time to have reduced uncertainty25

Related technologies: HUMS and engine management systems (EMS)

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6 Schematic of benefits of prognostics: reliability is enhanced for parts subjected to severe usage, while significant additional use is gained for parts subjected to less severe service; for some components, some other measure than time in operation is better measure of life consumption, but still unexpectedly mild usage will result in wasted component life owing to early replacement26

Health and usage monitoring systems for helicopters

The main gearbox in a helicopter is a single point of failure that can cause a crash, and it experiences similar gust loadings to the gearbox in a wind turbine. The main gearboxes of the largest current helicopters and largest current wind turbines have similar power specifications: 8000 shaft horsepower for the SH-60 (y6 MW), although helicopter rotors turn at a few hundred rpm instead of the few tens of rpm typical for wind turbines. Almost all HUMS include vibration monitoring, but use many different frequency ranges and analysis techniques. A recent review of HUMS was given by Zakrajsek et al.27 Many of the lessons learned in 25 years of HUMS application could be applied to wind turbines, for example: (i) besides safety improvements, maintenance improvements attributed to the 300 HUMS from Smiths Aerospace include:28 fault containment: reduced consequential damage owing to timely replacement of damaged components; improved diagnosis; reduction of unscheduled maintenance; and extension of component life. This study also cited the value of acquiring an accurate record of service conditions including loading and exceedances (overloads) (ii) another review29 of the results of 180 actions on 100 CH-146 Griffon helicopters credited HUMS with the following performance: 41% of the HUMS related maintenance actions led to the determination of the proper response to an exceedance (usage outside of allowable range of conditions); 19% allowed for adjustments to prevent accelerated wear of components; 17% prevented the need for additional test flights through verification of other sources of information; 12% prevented the replacement of components over $100 000 after an exceedance; and 11% prevented serious faults from worn components and loose mounts (iii) a review by Dora et al.30 cites reductions in maintenance man hours of 58% owing to the Goodrich integrated mechanical diagnostics HUMS (IMD-HUMS)

(iv) Cook31 cites the use of HUMS data to perform a fleetwide check after the break-up of a combiner transmission bearing. All other HUMS equipped aircraft were screened within 12 h, and since none showed the characteristic indications leading up to the failure, all were cleared to fly. The risk tolerance for wind turbines is much greater, as there is no loss of life associated with an incident. However, even one false alarm per 1000 h, the goal for HUMS,27 is at least an order of magnitude higher than is acceptable for wind farm operators, whose machines operate continuously (.8700 h/year). Aircraft EMS

An early EMS on the A-7E (a single engine, carrier launched jet fighter/bomber) in the early 1970s was extraordinarily successful. Claimed benefits include:32 accident rate owing to engine failure reduced by 90%, maintenance man hour/flight hour rate reduced by 66% and overall accident rate reduced by 66%. One of the reasons this system was so successful was that it was developed by engineers on aircraft carriers, who knew the application, the service environment and the maintenance environment. Also, they embedded much of their application specific knowledge in ‘hints’ to help with troubleshooting.32 The system also recorded ‘life usage indices’, a primitive load counting system (surrogate for component life consumption), but these data were not used by the Navy.

Distributed sensor networks Some of the sensing technologies described above require a few very capable, expensive sensors. Others require large, sophisticated and expensive signal processing to resolve and isolate fault signatures. These drawbacks are exacerbated by the increasing scale of wind machines; as blades get longer, the sensors to detect damage in the blades must function both over a longer range and with higher resolution. One solution to the issue of scaling is a distributed sensor network. A large number of simple, inexpensive

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sensors with simple local processors and short range communications can replace these complex, expensive sensors. Since each simple sensor monitors only a small area, low resolution, short range sensors can provide a great deal of capability. Common capabilities of distributed sensor networks include: (i) local processing: transmit answers, not raw data: saves power, simplifies analysis (ii) local coordination: use multiple observations of a single phenomenon to verify and classify the occurrence of the phenomenon: improves reliability and specificity of reported conditions (iii) energy scavenging: the sensor/processor nodes are self-powered by vibration, pressure changes, solar cells or other means: no wiring is required for power, and susceptibility to electromagnetic interference is reduced (iv) wireless communications: often, but not always, provides advantages of ease and economy of installation and flexibility of network topology. Current research programmes are exploring the application of distributed sensor networks to such diverse problems as tracking military vehicles,33,34 and condition monitoring of manufacturing plant35 and of thermal protection systems in spacecraft.36,37 This is a promising area for development of condition monitoring for rotor blades.

Condition monitoring: rotors and blades As blades have grown to .50 m in length and .17 t in weight,11 they have come to be important both in the cost of the wind machines (Fig. 1) and in the total tower head mass, which ranges from 310 to y500 t for 5 MW turbines.11 With these increases has come an increasing need to understand and address the many problems experienced by the blades. Foremost, wind turbine rotors must survive .108 stress cycles in a 20 year lifetime, so fatigue is a critical issue. Additionally, for the service environment of wind turbines, both creep fatigue and corrosion fatigue are important, and these are even less well quantified than ordinary fatigue. These effects show up as cracks and delaminations in the composite blades. Other issues include: (i) rotor imbalance and aerodynamic asymmetry, which causes even larger stress amplitudes than normal operation, shortening fatigue life; additional vibrations of the nacelle and tower; and also variations in rotor speed, which may cause mismatch in output power waveform38 (ii) moisture uptake, which causes additional imbalance when non-uniform; enhances creep; may attack the glass fibre/matrix interface, degrading material properties of the composite; and may increase growth of existing cracks owing to cyclical freezing (iii) icing, which causes surface roughness; may increase growth of existing cracks; and may cause imbalance (iv) surface roughness, which causes early stall of aerofoil, reducing maximum power output (v) impact: damage during fabrication, transport, construction, or in service may cause delaminations.

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State of the art: condition monitoring in rotors Rotor imbalance and aerodynamic asymmetry

Rotor imbalance and aerodynamic asymmetry may be caused by manufacturing defects, non-uniform accumulation of ice or moisture, or accumulated damage to the rotor blades. Both cause the rotor speed to vary within each rotation. Severe imbalance may require stoppage of the turbine until either the ice melts or until service may be performed to rebalance the rotor. For these issues, there are two common methods of detection. The first and the simplest is to detect variations in the rotor speed owing to asymmetry through variations in the power spectral density (PSD) of the electrical output recorded by the turbine’s supervisory control and data acquisition (SCADA) system, where a peak at the rotor frequency indicates either rotor imbalance or aerodynamic asymmetry owing to yaw error.38,39 Another analysis technique, bicorrelation, has also been applied to the output power to detect such asymmetries.38 Detection methods based on the output power have the advantage that no additional sensors or instrumentation are required. The second approach to detecting rotor imbalances is monitoring transverse oscillations of the nacelle;39 the imbalance causes a periodic force in the plane of the rotor to act on the nacelle. The magnitude of this force is determined by the magnitude of the asymmetry. This approach requires adding accelerometers to the nacelle. Geibhardt18 combined fuzzy logic and genetic algorithms with condition monitoring to detect these faults in a wind turbine. The combined method allowed reasonable discrimination among normal operation, aerodynamic asymmetry, rotor imbalance and unknown faults. Surface roughness and overall performance

Surface roughness, whether caused by erosion, icing, insects, or otherwise, can be detected by its effect on the ‘power characteristic’ of the turbine.40,41 The power characteristic is defined as the functional relation between wind speed and output power. This technique is also employed by CleverFarm;19 for tracking changes, an anemometer mounted on the nacelle is sufficient.40 Another group has compared the performance of turbines in the same wind farm to detect degradations in individual machines as an increase in deviation from the average performance of the farm;42 they also track the total energy produced, comparing it with the projected performance to estimate the overall condition of the turbine. Stress, strain, acoustic emission (AE) and modal analysis

Early offline measurements of strain in rotors using fibre optic Bragg grating (FBG) strain gauges were reported in the literature.43 This group also attached ceramic piezoelectric transducers to the rotor to allow modal analysis to observe evolution of simulated damage.44 A combination of offline testing of microbend fibres to detect failure of adhesive joints, short range AE to detect crack growth, and accelerometers for modal analysis was examined by Sorensen et al.45 It was concluded that these three sensor types are economically feasible and functional for detecting damage in the largest current wind turbine blades; however, quantitative prediction of damage state and residual life from sensor signals remains unresolved.

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7 Optical condition monitoring in rotor blades:47 fibres sense strain at root and along face of blade and detect cracks along trailing edge as loss of continuity; data are transmitted wirelessly to stationary computer in nacelle and then shared by wind farm LAN; power is often supplied by slip rings; LM Glasfiber: image reproduced with permission

Some of these techniques have been adapted to online analysis, suitable for condition monitoring. Rademakers14 cites AE as a promising technique for detection and localisation of failures in wind turbine blades; Blanch and Dutton46 suggests that localisation and classification of damage using AEs seems feasible during operation in high winds. Blade manufacturers LM and Aerpac developed acceleration sensors used to detect excessive vibrations7 on prototype turbines. However, despite the positive results of Sorensen et al.,45 acceleration sensors have fallen out of favour relative to fibre optic techniques for monitoring blades (Fig. 7) because fibre optic techniques measure strain as well as vibration. Many techniques for fiber-optic measurements in composites are reviewed by Fernando and Degamber.98 A number of groups13,47–53 have implemented prototype systems for measuring strain online, in operating turbines, to track fatigue damage. These systems have several features in common. All use FBG strain gauges, which have several advantages over traditional wire or foil strain gauges. Fibre optic Bragg gratings are coupled by optical fibre, so they are insensitive to electromagnetic interference (EMI) and lightning. Fibre optic Bragg gratings also have better fatigue life and better long term stability than traditional strain gauges. The price to be paid is in greater complexity, the much greater size and mass of the unit that interrogates the gratings to measure strain and the high price of such systems. Also, FBGs are even more sensitive to temperature variations than traditional strain gauges, adding further complexity. Approximately 10 FBG strain gauges per blade are employed by all the groups cited above, distributed as depicted in Fig. 7. This number allows the measurement of the root bending moment in both flapwise and chordwise directions, as well as the bending deflection at one or more points further out on the blade. The data collected by the FBGs are used to determine the load history of each blade. This load history combined with laboratory fatigue measurements (S–N curves) allows some estimate to be made of the

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fractional fatigue life remaining. None of the systems has sufficient resolution to detect the evolution of damage in the blade; they track the load history and use the S–N curves to estimate the remaining time to failure. Besides the load history, several of these groups49,51,52 claim the capability of using the instantaneous load measurements to drive individual adjustment of the blade pitch. These fine adjustments can reduce the peak bending loads on the blades by 15–30% and the tower yaw moment by 10–30%.49 With four commercial suppliers demonstrating strain monitoring in wind turbine blades, the technical capability is clearly achieved. All four are only prototypes and technology demonstrators, although one manufacturer (LM) has announced plans to offer strain monitoring as an option on its largest blades.49 A much simpler fibre optic technique was reported in the literature13,47 monitoring of continuity of optical fibres to detect incipient cracking or buckling at the trailing edge of the rotor blade, where fatigue cracks form first. Early efforts to model the changes in mechanical properties of composites owing to the effects of the harsh service environment (salt water, extreme temperature) are under way at Risø in Denmark.54 This group has developed a thermodynamic framework using internal state variables to account for moisture, temperature and other environmental effects. This work may lead to a better understanding of the physics of environmental damage in the composites, improving the accuracy of predictions of life under the conditions of actual service. Related work is summarised in the literature.55,56

Related technologies: condition monitoring in rotors Technologies developed for non-destructive evaluation of composite pressure vessels may be applicable to wind turbine blades. These include more advanced, online AE57 and active ultrasonic methods.58,59 Sensors for humidity and environmental effects may also be applicable. Solid electrolyte electrochemical sensors that allow moisture determination in fibreglass and carbon fibre reinforced polymers have been demonstrated.60 Fibre optic sensors using evanescent waves to measure moisture content have been demonstrated in flight tests on aircraft.61 These sensors were interrogated with the same techniques as FBGs, by the same equipment, and could potentially be multiplexed on the same fibre.

Technology needs: condition monitoring in rotors Probabilistic representation of fatigue data for wind turbine materials

Prognosis requires a probabilistic treatment of fracture (see Fig. 8), which requires knowledge of the distribution of fatigue life and residual strength, not merely mean values. Residual strength in damage or defect controlled failure often follows a Weibull relation, in which the probability of failure Pf at a stress s is given by Pf ~1{ exp½{(s=b)a
where a and b are empirical parameters characterising the probability distribution. The gathering of these data is under way for many materials important in aerospace

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8 Probabilistic fatigue of E-glass/vinyl ester laminate:62 this case is 18 500 cycles at 35% of ultimate tensile stress; a and b are Weibull parameters (see text); extensive testing allows determination not only of average remaining strength but also of its probability distribution; similar tests allow determination of probability distribution of remaining fatigue life under particular load; 1 ksi51000 psi56.90 MPa

applications. A similar detailed understanding of materials used in wind turbine rotors, under relevant service conditions, will be necessary to realise the full benefit of prognosis for rotor applications. Models of physics of failure of wind turbine materials

Another critical component of prognosis is being able to model the evolution of damage from a given, measured state. Multiscale physical models are becoming capable of integrating these processes from the atomic scale to the scale of the part, giving an accurate picture of the kinetics of damage evolution. Such models are approaching maturity for metals (see Fig. 9), but need further development for reinforced polymers. For nanoscale reinforced polymers and multiscale composites, the physical mechanisms of failure are not well understood.55 More research is required both on experimental determination of the dominant physical mechanisms and on the appropriate multiscale models to predict the behaviour of these composites under service conditions. This area is the subject of continuing research in Europe.21 Cost of blade monitoring systems

The online condition monitoring systems described above are all too expensive for economical use in present onshore turbines. The major cost factors are: fibre optic sensors themselves, the integration of the sensors into the blades, the purchase cost of the instrumentation for reading the stresses and the maintenance of this instrumentation.48 The cost of each of these components is currently too high, and economies of scale would not sufficiently reduce production costs. Two alternative, parallel approaches to cost reduction are appropriate. The first, advocated by Rademakers,48 is a ‘top-down’ approach: make the existing equipment, which incorporates only a few expensive sensors, more affordable. An alternate approach is ‘bottom-up’: make distributed networks of simple sensors more capable. The latter approach seems a very promising direction for future research.

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a low frequency cycles: crack follows grain boundaries; b high frequency cycles: cracks cuts through grains 9 Example of multiscale modelling approach in which atomistic models of local damage are combined with finite element models of macroscale stresses to produce accurate simulation of fatigue failure in metals:63 similar physical failure models are needed for composite materials to allow accurate extrapolation of current damage signals to future performance in wind turbines

For offshore turbines, the benefits of condition monitoring are more likely to outweigh the costs of the monitoring system, since both the large scale of future offshore turbines and their limited accessibility magnify the cost of downtime. Also, the cost of the condition monitoring system tends to increase more slowly than the cost of the turbine as the scale increases.

Condition monitoring: drivetrain The drivetrain of a wind turbine consists of all of the rotating components downstream of the rotor: shafts, couplings, bearings, gearbox, generator and brakes. The gearbox and generator together comprise both the most costly and the least reliable components in wind turbines (see Figs. 1 and 2).8,9 Gearbox cost and reliability issues have led to the development of many research turbines, and one family of production turbines (Enercon) do not use a gearbox, but instead drive the generator directly from the rotor. This approach also reduces the number of bearings in the wind machine to two low speed bearings, at the cost of a large, multipole generator (84 poles in the 4.8 m diameter generator in the E-40 (64)) and sophisticated power electronics.65 The most important issue in the reliability of the drivetrain is wear of the gear teeth and bearings in the gearbox. The causes include: (i) particulates in the oil owing to contamination during assembly, corrosion and wear (ii) variations in rotor speed owing to imbalance, variations in wind speed, etc., which may cause the gear teeth to chatter, causing fretting and generating particles (iii) stress concentrations in gear teeth owing to wear or machining (iv) mechanical interference or other manufacturing problems such as heat treatments or surface finish out of specification (v) loss of oil or oil circulation.

Condition monitoring in gearboxes and generators Problems with gears and bearings are characterised by changes in the vibration signatures and in the size,

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recently released, which may be a sign of distress. Similar sensors are fitted to helicopter gearboxes69 and the jet turbine engines on the JSF.22 Vibration monitoring in gearbox and bearings

10 Correlation of wear particles with damage:8 for normal conditions, wear rate is small and near constant; however, as damage accumulates, both number and size of particles generated increase

number and total mass of particulates generated in the gearbox oil. In generators in wind turbines, y40% of failures are related to bearings, 38% to the stator and 10% to the rotor.66 Stator and rotor problems included phase imbalances and turn to turn short circuits. These problems are easily detected and diagnosed by monitoring the stator and rotor currents, or alternatively by identifying the generation of harmonics in the output power signal.66 Other problems with the generator cause a loss of efficiency that can be detected by monitoring the power characteristic.40 Watson and Xiang16 discuss the use of condition monitoring to assist in the detection of faults in a wind turbine generator, by measuring the electrical power data and applying a combination of Fourier and wavelet analyses. Two specific problems it was possible to identify were generator rotor misalignment and bearing failure. The method was able to detect the problem y2 months before final failure. Two further methods of monitoring the condition of the gearbox and related systems bear more extensive discussion: oil monitoring and vibration monitoring. Oil monitoring in gearbox

Maintaining oil that is free of hard particles, with controlled moisture, viscosity, acidity and temperature, is essential for the health of the bearings and gears in the drivetrain. Frequent oil changes and offline oil analysis are not optimal because of the issues of access to wind turbines, although offline analysis can provide a detailed diagnosis of a fault detected by online methods.67 Online and inline methods both continuously monitor the oil circulating in the gearbox. Online systems sample a small portion of the oil that is circulated, whereas inline monitoring measures all of the circulating oil. Online systems are the most common and are widely available commercially for ,$1000.68 The size and number of particles generated by wear indicate gear and bearing conditions (see Fig. 10). GE Energy (4) offers optical and electromagnetic online oil particle counters and oil condition sensors. Pressure drop across the oil filter can also be measured,1 for both lubricating oil and hydraulic oil;7 a sudden increase in pressure drop indicates that many particles have been

Vibration monitoring is all but mandated by German insurers.10 Most commercial systems for wind turbine gearboxes and bearings are adapted from other applications, but still function adequately for wind turbines. These systems cover a wide matrix of frequencies with accelerometers, displacement sensors, acoustic sensors and different analysis techniques.4–8 Figure 11 illustrates a correlation method called ‘frequency enveloping’ that amplifies signals characteristic of damage, whereas a correlation between damage and increases in the power spectral density in the ultrasonic frequency range is shown in Fig. 12. Combining vibration monitoring with sophisticated electromechanical models and measurements of the power characteristic of the turbines to increase the sensitivity and discrimination of fault detection is also being explored.16,21 All the systems cited above meet the requirements of the German insurers. Although the cost of such systems is still high, detection of damage to the gearbox seems to be a solved problem. However, there is still value to be added in prognosis of the gearbox and bearings. On the other hand, many important vibrations occur at audio frequencies, so outside the German market the biggest competition for these systems is manual or remote aural inspection; in the USA, many operators believe this level of protection to be sufficient.1 In aural inspection, a technician listens to the sounds made by the operating wind turbine to assess its condition. The economic benefits of a more sophisticated monitoring system must be clearly demonstrated.

Related technologies: condition monitoring in gearboxes and bearings Oil condition monitoring

Other specialised technology developed for monitoring gearbox oil in aviation may be applicable to wind turbines. Some of these include the wear site sensor (WSS) from Smiths Aerospace,70 higher resolution particle counters using Fraunhofer diffraction71 and particle counters that operate on other physical principles such as capacitance or electrostatic charge. Also, as non-ferrous and hybrid bearings, such as silicon nitride balls in a steel race, become more common, particle counters and analysers that rely on the debris to be magnetic or electrically conducting will become less useful.68 Vibration and stress wave monitoring

Physical models of the waveforms of vibrations generated by damaged components and of the propagation of these vibrations may improve the accuracy with which the state of the components may be determined, and the discrimination between damage to different components and between different types of damage. Some of these models have been reviewed by Holm-Hansen and Gao.72 Multisensor condition monitoring and sensor fusion

Two approaches combining oil debris monitoring (ODM) with vibration monitoring to give greater confidence in measurement of the current condition of rotating bearings have been reported. Dempsey and

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a spectrum of failing bearing; b spectrum of healthy bearing 11 Detection of damaged bearing by vibration frequency enveloping:4 damaged bearing shows strong indication in envelope signal, while healthy bearing shows no indication of fault; difference in envelope signal is much more apparent than that in raw spectral data from vibration sensor

Afjeh73 used fuzzy logic to combine the signals, leading to a single indication of condition (see Fig. 13). Roemer et al.,74 in contrast, carried the analysis a step further, combining it with models of the physics of damage progression and probabilistic fatigue data to produce a prognostic forecast of the lifetime of a particular bearing under specific loading conditions. These experiments are discussed in greater detail in section ‘Physics of failure: models, experimental data’.

Technology needs: condition monitoring in gearbox and bearings Prognosis

Perhaps because of the extensive history of condition monitoring in helicopters and jet engines, prognosis of gearboxes has become highly developed. The alloys and conditions of gears in wind turbines are not very different from those used in helicopters. These gearboxes have similar power requirements, although the speeds differ by about an order of magnitude. The present fracture models are approaching the capability of quantitative prediction of the evolution of damage in these gears (see Fig. 14), and current progress is rapid. Current research finding in the USA alone in this area amounts to tens of millions of dollars per year.75 Given the resources of the wind power community, the focus should be on careful tracking of the advances in monitoring and prognosis of gears and bearings for aerospace and on their adaptation to wind power. Economics

12 Stress wave energy of damaged bearing race:69 as bearing passes over damaged part of race, large peaks in stress wave energy are observed

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Condition monitoring in gears and bearings in wind turbines has become widespread, at least in Germany. Suppliers offer truly different systems that meet the requirements of German insurers. Given the standardised requirements for condition monitoring, the suppliers have an incentive to compete on price rather than on improving performance beyond the insurers’ standards. The cost of a typical vibration based condition monitoring system was estimated as $10 000–15 000 in 2003.14 Research to develop cheaper methods of achieving similar results, or to allow more accurate determination of the needs in terms of precision and accuracy of the diagnostic information, would be advantageous.

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a propagation through tooth area; b propagation through web area 14 Comparison of actual and computed crack trajectories in H-60 bevel gear76

13 Physical condition of gears against inputs (vibration and oil debris mass) and output of fuzzy logic model for sensor fusion:73 reading number increases with time in service; FM4 and NA4 reset are different measures of intensity of vibrations

Condition monitoring: other components Electronic controls and power electronics Electronic controls make up only y1% of the cost of a wind turbine (Fig. 1),9 but cause 13% of failures (Fig. 2),8 which suggests that effort to increase the reliability of the electronics may be warranted, even at significant cost. The problems in electronic controls have many causes, including vibration induced fatigue of wires and printed circuits, corrosion, water ingress or condensation and dust. Power electronics make up a very small fraction of the cost of constant speed wind turbines, but are very important to the cost of variable speed and direct drive machines. Problems in power electronics are often due to thermal management issues owing to overload or loss of cooling, or thermomechanical fatigue owing to variations in load.77 Unfortunately, very little time elapses between the first indication of trouble and catastrophic failure in these systems, offering little hope for prognosis of failure. The current condition monitoring systems for electronics monitor output voltage, current and phase and in

some cases temperatures inside the nacelle. Thermography has been suggested as a technique to detect damaged traces and overheating components, but it does not appear to have been applied in service.8 In 2004, Howard78 concluded that there was ‘no significant technology development effort in place’ for prognostics in medium power electronics, nor for either diagnostics or prognostics for high power electronics. Some early research addressing the prognosis of electronics has been sponsored by NASA79 and two programmes by the JSF Program Office.80,81 The latter programmes have met with limited success, while the former concluded that, particularly for power electronics, the timescales for degradation are too fast for corrective action before failure. Perhaps redundant controls and power electronics, as used in aerospace applications, are called for in larger and offshore turbines. The electronic systems are unique in wind turbines in that redundancy is achievable within constraints of functionality, volume and mass. The cost of redundant systems should be considered against the expected loss of revenue, which could be very important in future wind machines. For the hypothetical 12 MW offshore turbine in section ‘Objectives of condition monitoring’, the cost of the electronic controls would be y$100 000. A triply redundant system costing $300 000 would be justified if it could prevent a single day’s lost production in each of the 20 years of service, or a single outage of 20 days during the 20 year service. Note that for offshore turbines, any unscheduled outage may require many more than 20 days of waiting for acceptable weather to service the turbine. Even for current 1.7 MW turbines, where the electronics cost y$17 000, a triply redundant system would be justified if it could prevent the loss of y30 days’ production in 20 years.

Support structure The newest turbines with blades over 50 m long require towers that are approaching and even exceeding 100 m

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15 Sensor for monitoring fatigue cracks in bolted and riveted structures83

in height. As shown in Fig. 1, the cost of the tower decreases as a fraction of the total cost, reaching 12% for a 3 MW turbine.9 The fractional cost of the foundation also decreases, accounting for only 2% of the cost of the 3 MW turbine.9 Neither tower nor foundation has accounted for any failures in 10 000 turbine years of operation.8 Nevertheless, with the growth of tower height .100 m, tower head mass .500 t, and continued growth in bending load, some investigation of condition monitoring is warranted. Matal et al.,82 Caselitz39 and the CleverFarm project19 have tested condition monitoring techniques for steel tubular towers, the current dominant technology; however, the steel tubular tower is reaching the limit of its capability. One industry expert contends that the ideal solution for taller towers would be a steel lattice spaceframe (like the Eiffel Tower), but that this design is limited by fatigue of the joints.1 Fatigue monitoring (see Fig. 15) may enable the use of this tower design. Lattice and hybrid lattice/tubular towers are among current proposals for offshore turbines, as pictured in Fig. 16.9 The support structure of onshore turbines consists of only the tower and foundation. For offshore turbines, a submerged support structure is added between tower and foundation (see Fig. 16). The need for condition monitoring of towers, support structures and foundations may differ from that for monitoring damage accumulation in other large, civil engineering structures such as bridges and buildings, because offshore wind turbines experience a harsher environment. Many of the techniques described for civil structures by Chong et al.85 are applicable to towers and foundations for wind turbines also. Other, specific issues for offshore towers,

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substructures and foundations may be addressed by specialised devices such as the chloride sensor.86

Technical and economic challenges in condition monitoring and prognosis Reliability of condition monitoring system, sensors and sensor data As stated in the introduction, the indications of faults reported by the condition monitoring system must be sufficiently specific and credible to serve as the sole basis of maintenance action. As with HUMS, the challenge is in raising the fraction of faults detected, detecting these faults as early as possible, and correctly identifying the faulty component, all while reducing false positive indications to an acceptable level. Improving the reliability and specificity of condition monitoring systems is a continuing area of research both in wind turbines and in aerospace.

Physics of failure: models, experimental data For rotors, gears and bearings, a better understanding of the physics of failure is required to take full advantage of condition information in prognosis of the components. For all three components, the issues are initiation and propagation of high cycle fatigue cracks. While extensive databases on fatigue of composites have been gathered,87–91 a more thorough understanding of the probabilistic effects of fatigue is missing. Empirical determination of the probability distribution of fatigue life requires many (y30) fatigue tests at each stress ratio R (the ratio of maximum to minimum applied stress) to provide sufficient statistical information.

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a concepts similar to those adopted by Talisman Energy;84 b concepts for floating structures not yet adopted9 16 Concepts for foundations for deep water offshore turbines

An example of the number and type of measurements required is given by Roemer et al.,74 who conducted rolling fatigue tests on a shaft surrounded by ball bearings. Using the results of 30 fatigue tests, they were able to determine the constants for models for the initiation and propagation of cracks leading to spall

from the shaft, and to the large vibration amplitude used as a failure criterion. The distribution of measured failures compared very favourably with those predicted by Monte-Carlo simulations (see Fig. 17). Even the extensive research reported in the literature is generally not sufficient to provide significant statistical

17 Experimental versus predicted distribution of failures and cumulative distribution function (CDF) for rolling bearing fatigue74

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sample sizes to obtain the necessary properties empirically.92 The many tests required at each R value must be repeated for a large number of environmental conditions (humidity, temperature, salt concentration, etc.). In his review of fatigue investigations of wind turbine materials, Kensche93 recommends further research on damage mechanisms involved with stochastic loadings, multiaxial stress, environmental effects and scaling effects. For gears and bearings, the American Gear Manufacturers Association (AGMA) provides design standards.90,94 However, these data are inadequate for prognostics.93 In addition to the full, three-dimensional stress field, which is now available through finite element simulations, statistical distributions are required for: variation of fracture and fatigue properties throughout the part, residual stress distributions and growth rates for small cracks.93 The specificity of the tests and the sheer volume of data required may seem daunting, but there is hope. Major research projects currently under way in Europe under the OPTIMAT programme20 include theoretical approaches to the effects of multiaxial stress states, environmental conditions, repairs and size effects on the fatigue of composites.95 Related research for aerospace applications is being conducted at the University of Delaware Center for Composite Materials,96 and for a wide variety of materials and components at the University of Iowa Center for computer aided design.97 The combination of theoretical and empirical research in this area will lead to the understanding required for accurate forecasting of future performance.

Fusion of sensor data, models, databases To allow prognostic forecasting, data from sensors must be combined with databases of material properties, service history both of the fleet and of the particular machine, models of damage progression and forecasts of future service conditions. Fusion of these disparate types of information is an area of active research, with some significant successes already achieved. Again, the key to adding value here is actionable information. The system must have a record of accuracy and reliability that gives confidence in its outputs, and present its outputs in a way that supports the decisionmaking process of the operator. It is also essential that performance data be shared among different operators. Rademakers14 gives a concrete example on how an overview of the entire fleet of a model of wind machines allows more accurate estimation of the risk of failure, and therefore a more accurate estimation of the maintenance reserve required. Such information is often considered proprietary, but it can be distributed either by free and open exchange of information, or by limited sharing information with a trusted third party, which promises to share only aggregate data. Such a third party might be a users’ group, the manufacturer, the maintenance contractor, or a government or international agency.

Business models The reliability of complex mechanical systems and the value added by condition monitoring are greatly influenced by the business models they support. This may be illustrated by an analogy with the changing business model for jet engines in commercial transport

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aircraft.1 In this application, 95% of the lifecycle cost of the jet engine is in maintenance and replacement parts. Under the old model, engine manufacturers sold the jet engines to the aircraft manufacturers at a loss to increase their market share, and then made money on replacement parts. Maintenance is conducted in-house by the airline, or by third party contractors. The more maintenance hours required, the more revenue the third party maintainers make. The more replacement parts required, the more money the manufacturer makes. The new model is for engine manufacturers to lease propulsion as a service, at a fixed cost per flight hour, including all maintenance. Maintenance may be conducted by the manufacturer or by its contractors, but is not conducted by the airline. In this model, the manufacturer makes more money when fewer maintenance hours and fewer replacement parts are required. Some savings may be passed on to the customer, the airline, as a reduced rate (or rate of increase) of the fixed propulsion cost. Reliability has increased dramatically, and maintenance costs decreased significantly since the new model was implemented. Key advantages of the new model are that the manufacturer benefits most from the outcome that the customer desires most (high reliability at low cost), and that the one who is in the best position to implement technological advances (the manufacturer) also reaps the greatest profit from those advances. Wind turbines, like jet engines decades ago, are in transition to becoming a commodity. The operator does not buy a turbine because of its technology, but because of expectations of performance and return on investment. A business model that aligns the interests of suppliers and operators, and also directly matches the ability to make technological advances with the reward for doing so, will lead to the fastest evolution toward affordable, reliable wind power.

Value proposition Without profit, there will be no growth in the wind power industry, and no environmental benefits or technological innovations. In that light, the value proposition for condition monitoring includes: (i) reduction of unscheduled maintenance (ii) reduced cost of remaining maintenance through containment of faults and extension of the service life of healthy components (iii) reduced downtime through deferred maintenance, avoidance of unscheduled maintenance and avoidance of catastrophic failures of components (iv) new capabilities such as the ability to run a damaged machine safely at reduced capacity until repair, and extended operation of inaccessible turbines, reducing the economic risk associated with turbines in inaccessible sites. This added value comes at a cost. Besides the initial cost of purchase and integration of the condition monitoring system, false alarms may cause extra unscheduled maintenance visits, or cause the operator to lose faith in the system, and subsequently ignore indications of real problems. Misdiagnosis can result in replacement of the wrong part and additional maintenance to correct such errors. The requirement by the German insurers for either condition monitoring or a periodic rebuild of rotating

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components has strongly influenced the perceived value of condition monitoring. Without such a requirement, condition monitoring is a hedge against uncertain damage of uncertain cost at an uncertain, future time. With the requirement, the investment in condition monitoring protects against a known cost (total rebuild) at a known time (5 years/40 000 h), and may provide protection against unknowns at other times. This reduction in uncertainty greatly increases the likelihood of operators purchasing a condition monitoring system. This requirement has also changed the nature of the business for condition monitoring system suppliers. Beneficially, the business case for condition monitoring needed to be made to the few large insurance companies, rather than to the hundreds of individual wind farm operators. On the other hand, systems that are more capable than the minimum certification requirements are much more difficult to justify than previously, as certification has commodified condition monitoring of the drivetrain and generator.

Conclusions The present paper has reviewed the state of the art of condition monitoring in wind machines, related technologies from other industries and needs for technological development in each of the four major subsystems of wind turbines: rotors, drivetrain and generator, electronic controls and power electronics and support structure. In terms of value added by condition monitoring systems, the drivetrain has the greatest potential, with the rotor a close second but poised to overtake. The value added by monitoring the condition of the other systems is less certain. These potentials are reflected by the offerings of turbine manufacturers, with widespread adoption of condition monitoring in drivetrain components in Europe, spurred by the requirements of the German insurers. In the USA, manual visual and aural inspection is still common, even for these high value components. The potential of condition monitoring for rotors is clearly seen by the manufacturers, who offer many competing systems, but this value has not yet been proven to the customers. No commercial condition monitoring is currently offered for towers and foundations, nor for electronics beyond oversight by the supervisory control and data acquisition (SCADA) system. However, the reliability of both power electronics and electronic controls is a significant concern, especially for offshore installations. Related technologies such as machine damage prognosis could greatly increase the value of condition monitoring in the drivetrain and rotor of wind turbines. Much of the research in this area is generic and being conducted by the aerospace community for civil and military aviation. Some specific research will be required to apply the principles of prognosis to the wind power industry. Wind energy technology has advanced greatly over the past several decades, reducing the cost of energy to the point where it is competitive with other forms of electric power generation in many situations. Further advances in technology are required to enable commercial use of wind turbines in more difficult sites such as offshore locations. As wind turbines become larger and are located in more remote areas, both onshore and

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offshore, the value of condition monitoring increases. Condition monitoring will be enabling for the largest offshore turbines. One critical choice in maximising the value added and return on investment in a condition monitoring system is the development strategy: should there be a few expensive sensors or many cheap sensors? Local or centralised processing? How should government and industrial research be apportioned between adding capability and reducing cost? These questions should be a part of the discussions among the stakeholders about the future directions of research for wind power.

Acknowledgements The authors would like to thank their technical advisory group (Walt Musial, Clint Coleman and Gillian Smith), and also to thank the Electric Power Research Institute (EPRI) for permission to publish these results. The present work was supported by EPRI under contract number EP-P18751-C9259. The present paper is based on parts of EPRI Report 1013662, Programme on Technology Innovation: Materials Degradation in Wind Turbines, published in August 2006. One of the authors (Kerry Sullivan) gratefully acknowledges support from the National Science Foundation under NSF award number 0552548 and the UMass College of Engineering REU programme.

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