Prognostic Software Agents for Machinery Health Monitoring 1
Kevin P. Logan MACSEA Ltd. 163 Water Street Stonington, CT. 06378 860-535-3885
[email protected]
automated trending analysis, alarm prediction, fault prediction, and prognostic event logging. The prognostic software agent is a generic tool for maintenance personnel to implement CBM. It predicts future machinery faults and determines when maintenance should be carried out. By predicting machinery problems before they occur, unexpected breakdowns can be avoided. In the absence of significant trends, equipment overhaul periods may be rationally extended, thereby eliminating unnecessary maintenance work. The ability to predict future maintenance requirements leads to improved maintenance planning and cost management. Maintenance and repair decisions can be tied to actual plant operating conditions based on the severity of degrading trends and predicted plant problems.
Abstract— Increasing levels of machinery automation for systems health monitoring are providing operators with larger amounts of raw data. However, transforming massive amounts of data into information useful for effective condition-based maintenance (CBM) remains an arduous task. New technology is needed to continually monitor machinery, to identify impending failures, and to accurately predict its remaining useful life. Prognostic software agents can satisfy this growing need as higher levels of machinery automation raise the cost requirements of continuous monitoring beyond the levels of human and company feasibility. Software agent technologies that can automatically perform useful work as human assistants and can readily be integrated into existing automation system environments, represent viable tools to improve machinery reliability and reduce maintenance costs.
TABLE OF CONTENTS Software agents can be used to clone human intelligence, perform human-like reasoning, and interact with human clients. Agents can perform tedious, repetitive, timeconsuming, or analytically complex tasks on behalf of people who may not have the time or requisite skills to perform these tasks themselves. Agents can serve as expert assistants in monitoring, troubleshooting, and predicting failures in complex machinery processes.
1. INTRODUCTION ..............................................1 2. CREATING AGENT INTELLIGENCE ...............2 3. DIAGNOSTIC TECHNOLOGY EMBEDDED INTO AGENTS ................................................4 4. PROGNOSTIC SOFTWARE AGENTS ...............7 5. USCGC HEALY IMPLEMENTATION ...........9 6. CONCLUSIONS .............................................12 REFERENCES ...................................................13 BIOGRAPHY .....................................................13
Imparting intelligent processing functions into software agents will allow maintenance organizations to leverage valuable “corporate” knowledge across geographically distributed machinery plants, such as aircraft or ship fleets. Agents can be distributed when and where needed to enhance fleet operations, performance, and readiness. Their intelligence can be upgraded remotely. The human-agent team can provide higher levels of productivity at practically the same cost as that of just the human resource alone.
1. INTRODUCTION A primary goal for introducing new technology into shipbuilding and operations is minimizing life cycle cost. Among the major cost factors for ship operations are manning and maintenance. Design strategies focused on reducing crew size involve more extensive automation of machinery monitoring functions, primarily through increased sensor instrumentation. Other design strategies, particularly for naval vessels, focus on increasing survivability through decentralized, distributed systems, resulting in more complex machinery plants, with redundant systems. Such designs also generate increased requirements for monitoring
This paper describes intelligent prognostic software agents for real-time machinery monitoring applications. The main functions of the prognostic agent include machinery performance assessment, historical data archiving, 1
IEEEAC paper #1311, Updated September 19, 2002
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geographically distributed work environment, such as a ship fleet. Once constructed, the agents become a valuable resource that can be distributed when and where needed to enhance operations and performance.
and control automation. Following either of these strategies creates maintenancerelated operational challenges. More complex machinery plants generate requirements for more extensive equipment monitoring, as well as more comprehensive knowledge and skills on the part of operations and maintenance crews. Crew training requirements will grow with system complexity. There will be more machinery data to monitor, with fewer people having less time to analyze it. Yet with reduced manning, the importance of keeping a constant vigilance in machinery performance assessment will never be greater, as the attendance to machinery failures will draw a larger percentage of available onboard human resources. Intelligent Software Maintenance
Agents
for
Interact with human clients – Agents are designed to interact with people. They are not intended to replace people. Instead, they are a valuable extension of the human client. Agents tirelessly and autonomously perform their work in the background. This allows the crew to address higher-level problems within their work environment. Through an agent’s direct interaction capabilities, they inform the crew of equipment status and report important operational events. The agents can also be called upon demand to perform specific tasks when a crewmember needs them done. Software agents can work autonomously, as the crew performs other work in parallel. The human-agent team can provide higher levels of productivity at practically the same cost as that of just the human resource alone. By applying software agents as workforce productivity multipliers, the organization is able to leverage its intellectual assets for maximum effectiveness and profitability.
Condition-Based
The benefits of machinery health monitoring through condition-based maintenance technologies have been clearly established [1-3]. However, new technology is needed to continuously and automatically monitor machinery, to identify impending failures, and to accurately predict its remaining useful life. Intelligent software agents will play an increasingly important role in monitoring, controlling, and troubleshooting complex machinery processes aboard future ships [4]. A key benefit of software agents is their ability to automatically perform complex information processing tasks without being constantly controlled by people [5-9]. Software agents can assist the crew in complex decisionmaking and other knowledge processing tasks related to CBM requirements. The role of prognostic software agents will grow as higher levels of plant automation raise the cost requirements of continuous machinery monitoring and CBM beyond what organizations can feasibly implement. New agent technologies can be deployed to automatically monitor and analyze hundreds of thousands of data points, while being integrated into existing shipboard automation system environments.
MACSEA has developed a machinery diagnostic and prognostic agent building system called DEXTER [4]. It is now possible for users to rapidly create and deploy intelligent prognostic agents for real-time machinery monitoring applications. Through a standard software interface [10], agents can readily be configured to “plugand-play” with most existing automation systems. Software tools are provided to build diagnostic knowledgebases that give the agents their artificial intelligence for monitoring and prognostic tasks.
2. CREATING AGENT INTELLIGENCE Building Diagnostic Knowledgebases A knowledgebase encapsulates both rare and expensive engineering knowledge about a machinery plant and its equipment. A diagnostic knowledgebase is developed by performing an expert-level analysis of machinery failure modes and their associated effects. This process is called a Failure Mode and Effects Analysis (FMEA) of the machinery plant. The FMEA involves enumerating all common or likely machinery faults. Information sources drawn from for the FMEA can include historical experience, manufacturers’ troubleshooting information, and assessments of industry experts. Each fault is characterized by its measurable symptoms in the plant, as monitored by the available sensor instrumentation. A symptom is defined as an abnormal condition, such as a particular temperature measuring HIGH, with respect to a set point limit. Predicted symptoms are derived by prognostic agents.
The main functions of software agents are: Clone human intelligence – Rare or valuable human intelligence is replicated for use by other people who may be less experienced about a particular application. Perform human-like reasoning - Software agents are empowered with computer representations of human knowledge, allowing them to perform information processing tasks on behalf of their human counterparts. Agents can perform tedious, repetitive, time-consuming, or analytically complex tasks on behalf of people who may not have the time or requisite skills to perform these tasks themselves. The ability to impart intelligent processing functions into artificially constructed agents allows organizations to leverage valuable knowledge across a 0-7803-7651-X/03/$17.009 © 2003 IEEE
The FMEA forms the basis of diagnostic knowledge about the machinery plant. A comprehensive FMEA typically 2
Any corrective actions or special instructions that the maintenance engineer should follow if the fault is detected can also be entered. The information entered on this form will be displayed to users when this fault is detected by one of the prognostic agents.
involves a substantial amount of time and effort. Because of this, any knowledgebase created from the FMEA becomes a valuable "corporate" information asset, particularly when coupled with intelligent software agents as part of a condition-based, reliability-centered maintenance program. This knowledge can be distributed and exploited across the entire maintenance operation, whether it is a single ship or across a fleet of ships located around the globe. Even the newest maintenance crewmember can immediately benefit from the diagnostic knowledge assets that become the embedded intelligence of prognostic software agents. The bottom line will be improved reliability and readiness through avoiding, reducing, or eliminating machinery failures.
How Agents Use Knowledgebases DEXTER's software agents are “knowledgebase-centric”, which means that each agent is linked to a specific knowledgebase. The knowledgebase defines both the data source and the specific data points that an agent will monitor and analyze. By designing the agents to be knowledgebasecentric, a minimal amount of setup information is required for configuring an agent. You simply select a knowledgebase to be used by your agent and it then knows exactly which set of data points to monitor.
The DEXTER software is used to enter FMEA results into one or more knowledgebases. For each machinery fault identified, the fault and symptom information is entered into a form as shown in figure 1. For each fault entered, the associated symptoms are selected from a drop-down list box of real-time sensor signals available from the automation interface. To enter a fault symptom, the user simply selects a sensor signal and then indicates the alarm state (HIGH or LOW) expected when the fault occurs.
Multiple knowledgebases can be created, with each pertaining to a separate machinery plant, specific system within a plant, or even an individual piece of equipment. The scope of the monitoring and analysis functions of an agent is defined by the diagnostic coverage of the knowledgebase to which it is linked.
Figure 1 – Form for Building Diagnostic Knowledgebases (DEXTER )
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Agent 1-1 Knowledgebase 1
DEXTER
Agent 1-2 Agent 1-3
TM
Knowledgebase 2 Agent 2-1
Knowledgebase N
Agent N-1 Agent N-2
Figure 2 – Separate Knowledgebases and Agents Created to Monitor Different Machinery Neural Networks - Transforming Knowledge into Artificial Intelligence
Machinery Performance Assessment Machinery performance assessment is accomplished by deriving baseline performance relationships for the salient parameters across the machinery operating range. Sensor measurements are then compared to the baseline and performance deviations are computed. During anomaly prediction, these deviations are trended to determine if they will exceed statistical limits for the machinery process. Predicted deviations falling beyond established operating thresholds are considered as anomalous behavior and are flagged as predicted alarms. Baseline relationships typically consider one parameter, such as a temperature or pressure, as being dependent on another independent parameter. Independent parameters typically include a load level or speed level. For regulated systems, the regulation set point can be used as a constant baseline of comparison. Figure 3 shows an example of a typical baseline performance relationship. Measurements of charge air pressure are plotted against engine load. A regression curve has been fitted through the raw data to derive an equation expressing the mathematical relationship between the two measurements. The equation can now be used as a baseline. It is used to estimate expected values of charge air pressure for any engine load.
DEXTER uses neural networks for agent reasoning of machinery faults. The neural network automatically learns to associate patterns of alarm conditions with the machinery faults entered into knowledgebases. Once the fault-symptom associations have been learned, an agent uses this knowledge to perform prognostics. The agent predicts future alarm conditions from real-time sensor inputs. It then recalls from its neural network memory those faults having symptom patterns most closely matching predicted alarms. DEXTER’s probabilistic neural network learning occurs instantaneously, as compared to other neural network techniques, allowing prognostic agents to be rapidly built and modified. A separate neural network is created for each knowledgebase. The agent uses the neural network associated with the knowledgebase to which agent it is attached.
3. DIAGNOSTIC TECHNOLOGY EMBEDDED INTO AGENTS
Assuming the baseline relationship is accurate, measured machinery performance can be expected to follow the baseline. If a machinery problem develops, its behavior will no longer follow the baseline and an anomaly between measured versus estimated values will occur. These
The processes involved with automated prognostics include machinery performance assessment, anomaly prediction, and probabilistic neural network reasoning. 0-7803-7651-X/03/$17.009 © 2003 IEEE
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Figure 3 – Typical Baseline Performance Relationship
mean of a Normal distribution is contained within plus and minus three Sigmas from the mean. Hence, the plus and minus 3 Sigma limits define a region within which the machinery performance deviations should normally vary. This normal region is illustrated in figure 4.
discrepancies can readily be detected by monitoring the computed deviation between measured and estimated performance parameters. DEXTER agents automatically compute, record, and analyze deviation values. Deviation values are assessed on the basis of historical measurements, characterized by their statistical distributions. Deviations are assumed to be random variates having Normal or Gaussian distributions. These distributions are characterized by their mean and standard deviation parameters. The standard deviation is often referred to as Sigma. Over 99 percent of the random variation about the
By comparing machinery performance deviations to their statistical thresholds, anomalies can readily be detected and predicted. Deviation values that exceed their 3 Sigma limits are declared as alarm conditions. Figure 4 illustrates a low alarm condition for the plotted deviation parameter.
Figure 4 – Anomaly Detection By Monitoring Deviations from Baseline 0-7803-7651-X/03/$17.009 © 2003 IEEE
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Probabilistic Neural Network Assessment of Predicted Machinery Alarms
Machinery Anomaly Prediction In order to predict future faults, prognostic agents statistically analyze historical deviation data to detect any unusual or degrading performance trends. This trend analysis is entirely automatic. Each performance trend is statistically evaluated for significance before it is declared as a valid trend. Valid trends are then used to predict alarm conditions likely to occur in the future. The system identifies when any monitored deviation parameter will exceed operating thresholds by extrapolating the trend out into the future. If an alarm threshold will be exceeded within a prespecified prediction horizon, then a predicted alarm is declared. The exact point in time that an alarm is predicted to occur correlates directly with the predicted time to failure of the related machinery component. The prediction process is illustrated in figure 5. The Maximum History Length and the Prediction Horizon are prognostic agent attributes that the user can change to suit his particular requirements.
Missed diagnostic calls and false calls translate directly into added maintenance costs, either from unexpected machinery failures or unnecessary maintenance activities. The robustness of a diagnostic system therefore directly impacts maintenance expenditures, as well as equipment reliability. An artificial neural network is one type of AI reasoning technique known for its robustness in diagnostic applications [11]. Neural networks have several attractive features: • • • •
Able to learn from training examples, Capable of real-time pattern recognition, Capable of classifying novel input patterns not included in training data, Tolerant of noisy or incomplete input patterns.
Neural networks can learn fault/symptom associations typical of diagnostic pattern recognition applications [12]. Learning occurs through numerical adjustment of the network weight parameters. Because neural networks are tolerant of noisy or incomplete input patterns, they can be used to build more robust diagnostic systems than those that
All alarm conditions predicted in this manner are automatically assessed by the prognostic agent's neural network.
Predicted Time to Failure
PAST
FUTURE
Deviation from Baseline
HIGH Alarm
Predicted Alarm
LOW Alarm
Maximum History
Present
Prediction Horizon
TIME Figure 5 – Predicting Future Alarm Conditions from Historical Deviation Trends
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follow a rule-based approach. Even if one or more symptoms are missing, the network is still able to identify the fault with the closest symptom pattern, based on the training data that it has learned.
vectors. Small values of σ result in poor generalization, while larger values produce greater degrees of generalization, with the PNN interpolating between training sample points.
Most neural networks perform some sort of statistical computation on patterns contained in a training data set. These internal statistics are then used to classify new patterns presented as inputs to the trained network. The classification problem can be posed as an example of Bayesian classification, in which it is desired to categorize a set of inputs (symptoms). The categories or states of nature represent the different machinery fault possibilities in the context of diagnostic and prognostic applications.
4. PROGNOSTIC SOFTWARE AGENTS Agent Character Interface
Each software agent is assigned an animated character that provides an easy-to-use human interface employing the latest speech synthesis and recognition technologies, as well as mouse-click control. The agent’s character representation provides both speech and text balloon alerts when a machinery fault is detected. The prognostic agent character alerts the user by appearing on the computer screen only when it has predicted a problem in the machinery plant. At other times, the character remains invisible, with the agent silently working in the background. An agent can also be called up on-demand to check machinery conditions at any time. It is possible to deploy multiple simultaneous agents across a local area network, with each monitoring a separate part of the plant or various equipments. An agent configuration program is provided to assign the knowledgebase and configure other agent attributes. Figure 6 illustrates a typical prognostic agent alert. The agent character will appear on top of any application already running to alert the user of detected machinery faults. The user can then interact with the agent without interrupting other software applications currently in use.
DEXTER uses a Probabilistic Neural Network (PNN) technique for its automated diagnostic reasoning [13]. A PNN is used to classify abnormal alarm (symptom) patterns according to the faults that may have generated the alarm conditions. The PNN is pre-trained to learn the associations between a large number of faults and their corresponding symptom patterns. Once trained, the PNN can be connected to the machinery plant automation system to perform realtime diagnostics and prognostics. In practice, the PNN is trained from the results of the FMEA on the machinery plant. This effort typically results in a fault/symptom matrix by which a training vector is developed for each fault. The PNN estimates the class conditional probability density functions (PDFs) for each machinery fault according to the following equation:
fA ( X ) =
1 ( 2π )
p/ 2
σ
p
(X − X A ) T (X − X A ) exp − (1) 2 2σ
where:
X A = training pattern for fault A X = real-time input symptom pattern
σ p
= =
"smoothing parameter” dimensionality of measurement space.
This equation is applied to each fault and directly outputs the probability of the fault given a set of input symptoms. Conceptually, the input symptom vector is compared to the training symptom vector for the fault class. The closer the match between the two, the higher probability of the fault classification. For prognostic agents, the input vector, X, is comprised of the symptom pattern representing predicted alarm conditions. The only parameter to be adjusted in the PNN is the “smoothing” parameter, σ, which is related to the variance of the underlying PDF. This parameter effectively controls the ability of the PNN to generalize when the input vectors do not exactly match the training 0-7803-7651-X/03/$17.009 © 2003 IEEE
Figure 6 – Typical Prognostic Agent Alert
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Prognostic Agent Functions
Data Management of Machinery Performance History
Prognostic agents are designed to predict machinery problems at their earliest stage of development. Developing equipment problems can often be discovered by degrading performance trends in historical deviation data. The prognostic software agent is an important tool for maintenance personnel to implement effective Condition-Based Maintenance (CBM). The predictions of future machinery faults include estimated time to failure, and as such, can help determine when maintenance should be carried out. By predicting machinery problems before they occur, unexpected breakdowns can be avoided. In the absence of significant trends, equipment overhaul periods may be rationally extended, thereby eliminating unnecessary maintenance work. The ability to predict future maintenance requirements leads to improved maintenance planning, cost management, and plant reliability. Maintenance and repair decisions can be tied to actual plant operating conditions based on the severity of degrading trends and predicted plant problems.
The management of potentially large data histories is an important aspect of a prognostic agent designed to detect machinery performance degradations over time. In addition to the size of these histories for a potentially large number of monitored data points, access to data from both local and remote locations over networks is a prerequisite in today’s ship information systems. Considering the cost impact of unreliable data access in the context of real-time machinery monitoring, a proven, reliable, large-scale database management system is necessary. DEXTER uses Oracle software for data management within its software agent infrastructure. The choice of Oracle was based to a large extent on the number of companies using Oracle to deploy mission critical, network-distributed database applications. Beyond the reliability issue, Oracle has the capability to scale up to any size application one might face in the context of CBM-related deployment of prognostic software agents.
The primary function of the prognostic agent is to perform machinery anomaly predictions, as previously described, and to predict future machinery faults before they actually occur. The prognostic agent performs this analysis for all machinery components and data points associated with its knowledgebase.
DEXTER’s BRAWN program is used to configure and launch prognostic agents. Figure 8 shows the main prognostic agent properties screen. The key agent properties specified through this screen include the following: Knowledgebase – defines the agent's artificial intelligence and data points to monitor, Report Interval – defines how often the agent will perform its prognostic processes, Probability Threshold – fault probabilities must exceed this threshold to trigger user alerts, Minimum History – establishes the shortest time history required for prognostic processing, Maximum History – establishes the longest time history to maintain for prognostics, and Prediction Horizon – establishes how far into the future fault predictions will be made.
The major work tasks performed by a DEXTER prognostic agent include the following: • • • • • •
records historical data for all sensor signals in its knowledgebase, performs machinery anomaly prediction (as previously described), performs prognostics based on predicted alarm conditions, performs remaining useful life (time-to-failure) predictions, issues user alert upon fault prediction, and records all prognostic events to historical log.
Using its probabilistic neural network, the agent will calculate the probabilities of every fault in its knowledgebase. The computed fault probabilities will vary based on the degree of match between the alarm conditions predicted in the machinery plant and the symptoms specified for the faults in the knowledgebase. Closer matches will yield higher probabilities. The probability values range from 0.0 to 1.0 (100%). Faults having probability below the Probability Threshold are not reported by the agent.
Estimated Time-to-Failure
The prognostic agent will report the estimated time-tofailure associated with a predicted machinery fault, as illustrated in figure 7. This estimate is derived from the time available to run the equipment until the first predicted alarm associated with a given fault. The varying severity of different deviation trends may result in some alarms predicted to occur before others. By reporting the earliest predicted alarm, the user is given the maximum amount of time to take corrective action prior to actual equipment failure. 0-7803-7651-X/03/$17.009 © 2003 IEEE
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Figure 7 – Prognostic Results Presented by Agent
Figure 8 – DEXTER Screen for Prognostic Agent Creation
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in extremely harsh environments, requiring highly reliable ship systems and an operational availability of at least 90%. Since HEALY's crew size is only 75, crew maintenance during ship deployment is limited to emergency repairs and minor, periodic preventative maintenance activities. The USCG has focused on reducing crew maintenance workload while still maintaining a high level of readiness.
Prognostic Agent Example
After the initial user alert of a predicted fault is issued, the agent presents a list of predicted faults, as was shown in figure 7. The detailed fault description screen of figure 9 shows a direct comparison of Expected versus Predicted alarm conditions. For each predicted alarm, an estimate of the time until the alarm will occur is given. Any of the predicted alarms shown here can be selected to view a historical trend graph of the data. The trend graph shows an estimate of when the sensor value is predicted to reach an alarm condition if the trend continues, as illustrated in figure 10. Here, the computed trend line is shown passing through the past data. The trend line intersects the HIGH alarm threshold for charge air temperature in 0.81 hours from the current time. Similar trend graphs can be viewed for any predicted alarm
To help meet its operational requirements, several equipment condition monitoring technologies have been implemented on HEALY to closely monitor the state of the machinery plant, aid underway troubleshooting, and allow the operators to make well-informed decisions regarding allocation of their limited maintenance resources.
Figure 9 – Detailed Fault Screen Showing Why Agent Predicted Fault
The HEALY diesel-electric machinery plant includes four Sulzer 12ZAV40S (7920 kW) main diesel engines, four Westinghouse 7200 kW main generators, two GEC AC synchronous main motors, and one EMD 16-645F7B (2400 kW) auxiliary diesel generator. The diesel engines constitute a significant portion of the total preventive maintenance for the machinery, as most diesel-related maintenance activities traditionally have been timedirected, rather than condition-based. Hence, any improvements in how diesel engine health monitoring is
5. USCGC HEALY IMPLEMENTATION The HEALY (WAGB 20) is a 420-foot ocean-going icebreaker designed to conduct a wide range of research activities (see figure 11). The HEALY's primary mission is to serve as a high-latitude research platform. All ship systems are designed to function during extended winter operations in the Arctic and Antarctic regions. HEALY's missions may include unescorted, extended deployments 0-7803-7651-X/03/$17.009 © 2003 IEEE
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0.81 hours
Past Data Predicted Alarm
Figure 10 – Trend Graph Showing Predicted Alarm in 0.81 Hours
Each engine system was broken down into its major components. The HEALY engineering crew was interviewed to obtain their insights into common machinery problems, maintenance practices, and potential problems based on established practice. From this information, probable failure modes of the various system components were enumerated. The causes and effects (at the available sensors) were then traced out through the impacted systems, using piping and instrumentation diagrams as a tool, as well as a comprehensive physical survey of the machinery spaces onboard.
conducted can result in substantial cost and time savings, particularly considering the small crew size and the USCG limited experience with the operation of the commercial diesel engines aboard HEALY. The mission requirements associated with long deployments into inaccessible areas emphasized the need for robust machinery health monitoring tools that could assist the crew in both fault troubleshooting and, more importantly, failure prediction (i.e. prognostics). As part of a cooperative research effort between the USCG, MACSEA, the University of New Orleans, the Gulf Coast Region Maritime Technology Center, and the Navy's Office of Naval Research, a DEXTER machinery diagnostic system was installed aboard HEALY in early 2001. The system was customized to the HEALY's machinery plant by developing knowledgebases and deploying software agents for real-time machinery health assessment. HEALY Knowledgebase Fault Coverage
Component failure modes were assessed independently of each other, regardless of the fact that one failure mode may produce exactly the same effects as several others. Measurable effects were constrained to available sensor instrumentation in the machinery plant. While it is not always possible to definitively isolate a system fault down to a single component, by automatically identifying and rank-ordering likely candidates by probability, the remaining troubleshooting tasks will be far less time consuming.
A Failure Mode and Effects Analysis (FMEA) was performed on the following systems: • • • • •
main diesel engines auxiliary diesel generators steam, feed, and condensate system de-ionized water system, and auxiliary seawater system.
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6. CONCLUSIONS The levels of machinery automation in future ship designs will continue to increase, providing massive amounts of data for health monitoring of ship systems. The crews aboard future minimum-manned ships will be hard pressed to transform this raw data into information that supports an effective condition-based maintenance program. There will be more machinery data to monitor and fewer people with less time to analyze it. Yet with reduced manning, the importance of keeping a constant vigilance in machinery performance assessment will never be greater, as the attendance to machinery failures will draw a larger percentage of available onboard human resources. New software agent technology can continuously and automatically monitor machinery, can identify impending failures, and can predict its remaining useful life (or equivalently time to failure). Machinery performance monitoring is an area where the immediate exploitation of software agent technology can yield substantial economic benefits. Agents can perform tedious, repetitive, time-consuming, or analytically complex tasks on behalf of people who may not have the time or requisite skills to perform these tasks themselves. Software agents can serve as expert assistants in monitoring, troubleshooting, and predicting failures in complex machinery processes. Intelligent software agents can be deployed to automatically monitor and analyze hundreds of thousands of data points, while being integrated into existing shipboard automation system environments.
Figure 11 – USCGC HEALY
Machinery problems are often the result of people problems. Faults involving valves being open or closed when they shouldn't be are common shipboard occurrences, particularly with less experienced crews. Such "operational faults" may have only a minor impact, but in some scenarios, can have a catastrophic impact on safety and plant reliability, leading to machinery faults. A number of these operational faults were included in the FMEA. The FMEA for the HEALY resulted in a diagnostic knowledgebase containing 1730 individual fault specifications. In total, approximately 725 real-time sensor signals were used to specify the various system faults.
Software agents are empowered with computer representations of human knowledge, allowing them to perform information processing tasks on behalf of their human counterparts. The ability to impart intelligent processing functions into software agents will allow ship maintenance managers to leverage valuable diagnostic knowledge across a ship fleet to enhance readiness while proactively minimizing maintenance costs. Once constructed, the agents become a valuable resource that can be distributed when and where needed to enhance operations and performance. Software agents will become workforce productivity multipliers, as the human-agent team will provide higher levels of productivity at practically the same cost as that of just the human resource alone.
Ongoing Evaluation of Agent Deployment
Prognostic agents were placed into continuous operation for health assessment of the HEALY's machinery plant systems previously listed. In addition to providing crew alerts, the agents are configured to record all prognostic events to an event log file. The log file can be periodically retrieved from the ship for assessment by the cognizant shore side maintenance support staff. Agent findings can be examined to help assess immediate, short-term, and long-term maintenance requirements. The event log also provides valuable feedback on agent performance that can be used to further fine-tune the diagnostic knowledgebases. Evaluation of the onboard software agents is currently underway.
REFERENCES [1] Reliability-Centered Maintenance, Smith, A.M., McGraw-Hill, Inc., 1992. [2] Reliability-Centered Maintenance, 2nd Ed., Moubray, J., Industrial Press, Inc., 1997.
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[3] Jacobs, Kenneth, "Reducing Maintenance Workload Through Reliability-Centered Maintenance (RCM)", Naval Engineers Journal, July 1998.
BIOGRAPHY Kevin P. Logan is President and Founder of MACSEA Ltd., a company that develops machinery diagnostic systems and performs related research in artificial intelligence. He has been responsible for overall management and technical development throughout his company’s twenty-year history. He has performed applied research in the areas of vessel performance analysis and machinery diagnostics during his 27-year career, working with shipping companies, engine manufacturers, the Navy, the USCG, and various academic institutions. His most recent research interests include intelligent software agents and artificial neural networks for automated learning systems. He has investigated the application of neural networks for fault diagnostics, prognostics, and real-time learning of complex machinery behaviors characterized by relationships among plant process variables.
[4] Logan, K.P., "Intelligent Software Agents for Distributed Machinery Monitoring and Diagnostics", ASNE Manufacturing Tech. for Ship Construction and Repair Symposium, Bremerton, WA, Sept. 2002. [5] Autonomous Agents '97, Proc. of the First International Conference on Autonomous Agents, ACM Press, February 1997. [6] Autonomous Agents '98, Proc. of the Second International Conference on Autonomous Agents, ACM Press, May 1998. [7] Autonomous Agents '99, Proc. of the Third International Conference on Autonomous Agents, ACM Press, May 1999. [8] Intelligent Software Agents, Brenner, W., Zarnekow, R., and Wittig, H., Springer-Verlag, 1998. [9] Multiagent Systems – A Modern Approach to Distributed Artificial Intelligence, Weiss, G. – Editor, MIT Press, 2000. [10] DEXTER Interface Control Document, MACSEA Ltd., Sept. 2002. [11] Logan, K.P., "Automated Reasoning Techniques for Diesel Engine Diagnostic System," Marine Computers '91 Symposium on Computer Applications in the Marine Industry, Boston, MA, September 1991. [12] Logan, K.P., "Deep and Shallow Inference Mechanisms for Diesel Diagnostics Systems," Diesel Generator Operation, Maintenance, and Testing Seminar, Electric Power Research Institute, Orlando, FL, June 1990. [13] Logan, K.P., Galie,T.R, and Savage, C. “A Practical Application of Probabilistic Neural Networks To Machinery Failure Prevention,” Proc. of 54th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA., May 2000.
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