Review of Vibration Signal Processing Techniques Towards Gear

International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 04

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Review of Vibration Signal Processing Techniques Towards Gear Pairs Damage Identification Rusmir Bajrić1, Denijal Sprečić2, Ninoslav Zuber3 1

2

Coal Mines Kreka, Open pit mine Dubrave, Tuzla, Bosnia & Herzegovina Faculty of Mechanical Engineering, University of Tuzla, Tuzla, Bosnia & Herzegovina 3 Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

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Abstract—Research in damage of gear and gear pairs using vibration signals is still very attractive, because vibration signals from a gear pairs are complex in nature and not easy to interpret. Predicting gear pairs defects by analyzing changes in vibration signal of gears pairs in operation is a very reliable method. Therefore, a suitable vibration signal processing technique is necessary to extract defect information usually covered under noise of other gear pairs dynamic factors. This paper presents the results of an evaluation of vibration analysis techniques as a method for the gear and gear pairs condition assessment. The origin of vibration in gear pairs and useful definition of damage identification techniques are presented. The detection and assessment capability of some of the most effective vibration techniques are discussed and experimentally compared, concerning a multistage industrial gearbox. In particular, the results of estimated vibration signal processing techniques are compared. Advantages and disadvantages of estimated techniques having in mind specific limitation have been shown. Further research in damage identification of gear pairs have been pointed out.

Index Term— spiral bevel gear, gear pairs, gear damage, vibration signal processing

I. INTRODUCTION Gear pairs in gearboxes normally generate vibration and corresponding vibration signal could be used as reference characteristics when the gear is in good mechanical condition. If defects occur to one of the gears during operation, the faulty gearbox would result in serious damage. Changes in vibration signals are often an indication that the gear pair meshing condition is changing. Measuring vibration gearbox with the help of accelerometer mounting on the gearbox housing is one of the best methods for gear pair damage assessment. Therefore, condition monitoring of the gearbox system during its operation is crucial to prevent the system from malfunction that could cause damage or entire system shutdown [1].

R. B. Author is with the Coal Mines Kreka, Open pit mine Dubrave, Mechanical Structure and Vibration Research Division, Tuzla, Bosnia & Herzegovina, [email protected]

Up to now condition monitoring and damage identification of industrial gearboxes has received significant attention by researchers engaged in multidisciplinary activities. The rapid progress in materials technology, intelligent sensor technology, signal processing and information technologies brings new solutions to solve a variety of problems associated with failures of industrial gearboxes in a real operational environment accurately and efficiently [2]. There have been many investigations carried out to monitor and assess of industrial gearboxes using different techniques. These methods are well-established industrial practices and among them vibration signal processing technique is well known. However, since the vibration signals measured from gearboxes are nonstationary and transient in nature, when damage occurs it is even more interesting to carry out investigation. All those technique have some limitations and cannot be applied in all conditions, i.e. some types of failure cannot be detected by simple vibration methods. Hence it is more desirable to investigate possibilities of some of those methods. The simple spectral analysis is generally unable to detect gear damage at an early stage; for this reason, many researchers have proposed the application of other vibration assessment techniques for the early detection of damage symptoms. The aim of this paper, on the basis of experimental results is to evaluate and compare detection and diagnostic capabilities of some of vibration signal processing techniques. II. THE SOURCES OF GEAR VIBRATION Gears are very widely used in machines to transmit power from one shaft to another, usually with a change in speed and torque. The study of the dynamic behavior of gearboxes has received moderate attention in literature, but due the great dynamics complexity of gears, it remains an insufficiently understood area. In practice out of balance and bearing forces are active in gear dynamics, but also the geometry of the gear profile has a crucial effect on the vibration behavior. In general flexural vibration will be more important then torsional vibration because flexural vibrations are transferred directly to the housing via the bearings [1]. In practice, the situation is not so ideal, as the teeth deform under load, introducing a

118604-5454 IJET-IJENS @ August 2011 IJENS

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International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 04 „meshing error‟ or „transmission error‟, even when the tooth profiles are perfect. In addition there are geometric deviations from the ideal profiles, both intentional and unintentional. Since perfect gears cannot be made, there is always transmission error [3]. Looking at the gearbox vibration mechanism of the most important vibration sources are: time variations in the mass stiffness, caused by variation of the number of teeth in contact and variation in the stiffness of the individual teeth; dynamic effects due the deviation from the ideal tooth profile, in practice all gears contain teeth manufacturing errors, such as errors due to the gear cutting process, deviation in the mesh angle, deviations from the involute profile, surface roughness of the gears; oscillations on the sliding velocity, where during the transmit ion of power there will be rolling and slipping in the point of contact and also oscillation may occur because of stick-slip effects. Due to these mechanisms, amplitude or frequency vibration signal modulation may be caused, resulting in sideband structures around the tooth meshing frequency and its harmonics [1]. The fact that the vibration amplitude varies with the mean load also means that vibration measurements must only be compared for condition monitoring purposes for the same load each time. Sometimes the only fixed load that can be relied upon is zero loads, but in general this is not a good choice for monitoring purposes, because the teeth can lose contact and give rise to chaotic vibrations which are not very repeatable and which do not necessarily respond to faults in the gears [3]. III. VIBRATION GEAR AND GEAR PAIR DAMAGE IDENTIFICATION TECHNIQUES

Most modern techniques for gear damage detection are based on the analysis of vibration signals acquired from the gearbox casing. The common target is to detect the presence of fault, rarely the type of fault at an early stage of development and to monitor its evolution, in order to estimate the machine‟s residual life. It is well known that the most important components in gear vibration spectra are the tooth-meshing frequency and their harmonics, along with the modulation sidebands. Amplitude modulations are present when a gear meshes an eccentric gear or a gear riding on a bent or misaligned shaft. If there is a local gear fault, the gear angular velocity could change as a function of the rotation. As a result of the speed variation, frequency modulations could occur. In many cases, both amplitude and frequency modulations are present. The increasing in the number and the amplitude of such sidebands often indicates faulty conditions. Since modulating frequencies are caused by certain faults of machine components including gear, bearing, and shaft, the detection of the modulating signal is very useful to detect gearbox fault [5]. The appearance of side bands around the gear mesh harmonics can also be the result of bearing wear accompanied by movement of the gear shaft. Gear operation where the shaft axes experience self sustained oscillations within the limits of bearing clearances is another source of sidebands [15].Time waveform analysis is a very useful but often unattended diagnostic tool. It was widely used in early time of vibration signal processing but fell out of fashion with now present modern vibration signal analyzers. Machinery vibration

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diagnostics is based on the principle that the "forcing function" causing a machine to vibrate is found by measuring the frequencies of predominant peaks as multiples of the shaft speed. However, various machinery defects, such as misalignment and looseness, generate similar spectral patterns, and may easily be confused by an inexperienced analyst. The spectral pattern of both misalignment and looseness problems show an increase in the amplitude levels of peaks located at one two and/or three times the shaft speed. These types of spectral patterns present a typical situation in which examining the time waveform is often useful for determining the specific problem actually causing the machine vibration [6]. Assessment of the time waveform in correlation with the spectrum can often emphasize problems in collected data, high acceleration amplitudes, repetitive impact sources, low frequency sources and abnormal waveforms. These problems could be present within the vibration collected signals, but the anomalies in the waveform can often be so readily apparent that further investigation of the vibration spectral data is initiated as a result of noticing abnormal time waveform. Cepstrum analysis has been widely applied to gear monitoring. The cepstrum is well suited for the detection of sidebands in vibration spectra and for the estimation of their evolution during gear life. In addition, since the cepstrum estimates the average sideband spacing over a wide frequency range, it allows very accurate measurement of the sideband periodicity. It is therefore applicable to both detection and diagnosis of gear faults [5-7]. Time synchronous averaging is a signal processing technique that is used to extract repetitive signals from additive noise. This process requires an accurate measurement of the repetitive frequency of the desired signal or a signal that is synchronous with the desired signal. The raw data is then divided up into segments of equal length blocks related to the synchronous signal and averaged together. When sufficient averages are taken, the random noise is canceled, leaving an improved estimate of the desired signal. Time synchronous averaging is a feature extraction technique that have been used successively to gearbox condition monitoring. The residual signal is obtained by removing the primary meshing and shaft components along with their harmonics from the Time synchronous averaging signal [8]. In this paper, the above-mentioned analysis techniques are applied to experimental vibration data, concerning a gear pair affected by different unknown faults of complete gearbox mechanism. The capabilities of fault detection of discussed techniques are compared in particular. The sensitivity to damage severity of specific technique is assessed and the most effective vibration signal processing technique for assessment of gear pairs is selected. IV. EXPERIMENTAL RESULTS AND DISCUSSION Experiments were performed on 500 kW three stage conveyer belt gearboxes. First stage unit contains a spiral bevel gear pair and second and third stage of helical gear pairs. Experiments were carried out on five of those gearboxes that are in operation since 1985.


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calculated for all gear pairs in the experimental gearbox and summarized in Table . TABLE I Gear mesh frequencies calculations

Shaft S1 S2 S3 S4

Fig. 1. Three stage 500 kW conveyer belt “in situ” experimental gearbox.

Speed

Gear Teeth

GMF

998 RPM 16.63 Hz

A

16

499 RPM

B

32

8.32

C

32

242 RPM

D

66

4.03 Hz

E

26

81 RPM

F

78

A&B

15968 CPM

C&D

15968 CPM

266.13 Hz 266.13 Hz E&F

6290 CPM 104.84 Hz

1.35 Hz

A. Time waveform analysis Time waveforms can be very complex looking graphs, but can often detect problems which other technique cannot. Particular problems might generate very similar looking vibration spectra, but can generate waveforms which look very different and cause differing amplitudes. As a major argument for using time waveform is time period that could be associated with some form of damage or irregularities. Time waveform is also major source for any other analysis and proper usage of it can give reliably results.

Fig. 2. Three stage gearbox scheme. First gear pair is spiral bevel type, second and third are helical type. Input speed is 998 rpm and gearbox ratio is 12.5. Detail cinematic information and specific forcing frequency are presented in Table I.

In order to investigate vibration signal, the signals were picked up on a gearbox in three direction of each gear pair shaft bearing casing. In addition, a one-per-wheel revolution tachometer signal was taken using build in sensor of OneproD MVP_2C_two channel vibration analyzer. The results presented in this paper are relative to nominal pinion speed of 998 rpm-16.63 Hz and to unknown pinion load, due to the variation of conveyer belt load during operation. The digital signals were processed and analyzed using the VibGraph_vibration analysis software. Also, the signals were preprocessed by means of a synchronous averaging technique. Time synchronized signal records have 5.12 kHz sampling rate and 32.000 signal points. Periodic forces associated with meshing of gear teeth also excite vibration at specific frequencies. These gear mesh frequencies (GMF) are

Fig. 3. Band pass filter, time wave form. Period of 0.244 s representing second shaft gear par rotating speed impacts.

Figure 3 shows time wave form of experimental signal indication problems in gear pair shaft rotating with 0.244 periods, 4.09 Hz-246 rpm which is rotation of shaft of gearbox. This could not be concluded from frequency spectrum of the same experimental signal. Time waveforms show precisely how the vibration changed over the time. Every time gear teeth mesh together the vibration will change


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International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 04 therefore, the extent of the damage may be best assessed through review of the time waveform as has been presented. The time waveform captures those instantaneous changes transients and this is why it is very useful technique in gear damage assessment. B. Analysis of amplitude modulation The next analysis technique concentrates on the vibration content of the sidebands of the fundamental meshing frequency. The frequencies of 266 Hz and 104 Hz have been identified as gear mesh frequencies of the gear box. Sidebands of the gear mesh frequency are caused by amplitude modulations and can be very useful for gear damage assessment. The modulations can be caused by gear misalignments, eccentricities, wear of the meshing surfaces or any other problem that would cause the profiles of meshing teeth to deviate from their ideal geometry or tooth spacing errors. These errors cause the mesh point between the ring and pinion gears to wander during operation, causing the speed of the gears and shafts to accelerate and modulate. This causes the gear mesh energy to increase and decrease with the rotation.

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can not only be identified, but also associated to specific gears and shafts. C. Cepstrum analysis Cepstrum allows for detecting periodicities in frequency domain usually as results of modulation. The frequency spectrum technique will not have information if changes coming from the source or transmission path. Harmonics and sidebands in the spectrum represent the concentration of excitation energy caused by the rotation component and they typically used to detect any abnormality in the operation. Advantages of using the cepstrum in the gear damage identification in the situation of combined effects of the harmonics and sideband in the spectrum appear in the cepstrum as a small number of clear defined rahmonic peaks; it is therefore easier to identify changes in the system. It is able to detect the presence and growth of sideband and to extract the spectrum periodicity [10].

Fig. 5. Cepstrum- presenting periodicity in 0.12 s which is 8.12 Hz or rotation by driven shaft

Fig. 4. Vibration spectra-amplitude modulation events of pinion gear shaft rotating with 16.63 Hz.

Usually, the amplitude at the gear mesh frequency is not used to detect a gear a gear damage because others operating parameters such as loads can affect this amplitude. Figure 4 shows vibration frequency spectrum of experimental gearbox, presenting triple harmonics of pinion gear pair 266, 532 and 798 Hz. Amplitude sideband families are dominant at third harmonic of meshing frequency with spacing of 8.12 Hz. This frequency is rotation frequency of driven shaft. Because of high third pinion gear pair meshing frequency harmonic amplitude and high amplitude sideband around rotation of driven gear, it is identified as tooth wear problem of driven gear. Therefore, by monitoring the sideband contents, faults

Figure 5 shows how severe damage of driven gear is, presenting number of rahmonic of driven shaft rotation and driven gear as source of problem. D. Time synchronous averaging analysis Time synchronous averaging is method of background noise reduction in spectra of complex signals. For instance, it can be used to sort out the contribution from one individual shaft and its associated gears from the complex vibration signature of a multistage gearbox.


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efficient for detecting changes not easily notable in the spectrum. Major benefit of using cepstrum technique would be earlier damage identification because it is clear and easier to see changes as many authors pointed out. Time synchronous averaging is very efficient in damage localization as many other authors refer. For complete gearbox damage identification it is necessary to repeat the analysis with reference to each gear in the gearbox. There is need to access gear shaft with a reason to collect tachometer signal. Many authors do not point out this significant limitation of time synchronous averaging technique, especially its ability to implement in practice. It would be interesting in future to review some of vibration signal processing techniques towards damage identification of planetary gear mechanism. Due to its complex character planetary gear mechanisms require special attention.

[1]

Fig. 6. Time synchronous average waveform circular diagram of experimental measurement of pinion shaft

[2] [3]

The averaging attenuates random noise as well as nonsynchronous noise from shafts and gears whose rotational periods do not match the averaging period. This makes it possible to focus the analysis on the gears of a specific shaft. The idea is that it is easier to detect a defect in where the dominant frequency components expected in a healthy gear vibration signal have been removed. Figure 6 shows the time synchronous averaging circular plot and present change in order by tachometer connected to the pinion shaft. Shaft rotation speed is 993.6 rpm and period of one rotation of shaft is 0.06 seconds. Time averaged waveform did exclude vibration components from everything except the events related to the pinion shaft. It is possible to conclude there is three section of pinion gear where vibration signal is higher. This could indicate the wear of gear tooth in this area of pinion gear. The main advantage of the time synchronous averaging is the possibility to divide the complex vibration signal from a gearbox into simple ones for each shaft. A drawback is the need for more complicated measurement equipment. Additional sensors are required to measure the rotational speed. V. CONCLUSION This paper compares the effectiveness of some vibration analysis techniques for damage identification in gear pairs, on the basis of experimental results. In particular, the capability of approaches based on time, frequency, amplitude modulation and time synchronous averaging analysis has been discussed. Each technique, except synchronous averaging analysis is capable of damage identification and damage severity evaluation. Time waveform analysis is very efficient in damage identification but many author do not using it often. Amplitude modulation sensitiveness to gear pairs load is concluded. Change in the amplitude at mesh frequency occurs upon changes in load. Cepstrum technique appears to be

[4] [5]

[6] [7] [8]

[9]

[10]

[11] [12]

[13]

[14]

REFERENCES A. de Kraker M.J.L. Stakenborg, 1986, “Cepstrum analysis as a useful supplement to spectrum analysis for gear-box monitoring”, Experimental stress analysis: proceedings of the 8th international conference, Amsterdam, Netherlands, May 12-16, p. 181-190, Rao, B. K. N., “Handbook of Condition Monitoring”, Elsevier Advanced Technology, Oxford 1996 Robert Bond Randall, “Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications”, Wiley 2011 Randall, R. B., 1982, “A New Method of Modeling Gear Faults", ASME Journal of Mechanical Design, April 1982, Vol. 104, p. 259-267. Fan, X., and M.J. Zuo., 2006, “Gearbox fault detection using Hilbert and wavelet packet transform”, Mechanical Systems and Signal Processing 20(4), p. 966-982 Timothy A Dunton, “An Introduction to Time Waveform Analysis”, Universal Technologies, Inc. RANDALL R.B., HEE J. 1981, “Cepstrum analysis”, Brüel & Kjær Technical Review, No. 3, 3-40. Komgon, N.C. Mureithi, N. Lakis, A. Thomas, M., 2007, “On the Use of Time Synchronous Averaging, Independent Component Analysis and Support Vector Machines for Bearing Fault Diagnosis”, 1st International Conference on Industrial Risk Engineering, CIRI, Montréal, Canada, p.610-624. P. D. McFadden, “Detecting fatigue Cracks in Gears by Amplitude and Phase Demodulation of the Meshing Vibration”, Journal of Vibration, Acoustics, Stress and Reliability in Design 108 (1986) p. 165-170. B.D. Forrester, “Advanced Vibration Analysis Techniques for Fault Detection and Diagnosis in Geared Transmission Systems”, Ph.D. Thesis, Swinburne University of Technology, Australia, 1996. Leon Cohen, Hunter College, “Time-frequency analysis”, Prentice Hall Inc., 1995. Forrester, B.D., “Analysis of gear vibration in the time-frequency domain”, in Current Practices and Trends in Mechanical Failure Prevention, edited by H.C. Pussy and S.C. Pussy (Vibration Institute, Willowbrook, IL), pp. 225-234, 1990. J. Hong Suh, S. R.T. Kumara, S. P. Mysore, “ Machinery Fault Diagnosis and Prognosis: Application of Advanced Signal Processing Techniques” CIRP Annals-Manufacturing Technology, Volume 48, Issue 1, 1999, pp. 317-320 S. N. Engin, K. Gülez and M. N. M. Badi, “Advanced Signal Processing Techniques for Faults Diagnostics-a Review”, Mathematical and Computational Applications, Vol. 4 No. 2, pp. 121-136, 1999.

[15] Dr. Alexej V. Barkov, Dr. Natalia A. Barkova “Diagnostics of Gearing and Geared Couplings Using Envelope Spectrum Methods” VibroAcoustical Systems and Technologies Inc., Saint-Petersburg, Russia


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