Vibration analysis is a standout amongst the most effective strategies present for diagnosing the health condition of rotating machinery. This paper deals with the vibration based gearbox health condition-monitoring techniques for diagnosing and classifying the gearbox faults. The statistical feature extraction has been done using the discrete wavelet

transform whereas the fault classification has been done using support vector machine. The results reveal that these vibration based diagnostic techniques are successful in gearbox health condition monitoring. National Renewable

Energy Laboratory (NREL) provided the data used in this paper

Naidu, VPS

English

National Aerospace Laboratories Institutional Repository

NCVSPICE2K18, JULY 19-20, 2018  ISBN: 978-93-85100-99-4 Department of ECE, University College of Engineering (A), JNTUK, Kakinada.  Page | 68   Gearbox Health Condition Monitoring: Vibration Analysis Rajani J and M. Srinivas            VPS Naidu        Department of Avionics      Multi Sensor Data Fusion Lab      Inst. of Science & Technology, JNTUK         CSIR- National Aerospace Laboratories      Kakinada, India         Bangalore, India    rajani.jeeri@gmail.com & mallimoggla@gmail.com      vpsnaidu@nal.res.in  Abstract—Vibration analysis is a standout amongst the most effective strategies present for diagnosing the health condition of rotating machinery. This paper deals with the vibration based gearbox health condition-monitoring techniques for diagnosing and classifying the gearbox faults. The statistical feature extraction has been done using the discrete wavelet transform whereas the fault classification has been done using support vector machine. The results reveal that these vibration based diagnostic techniques are successful in gearbox health condition monitoring. National Renewable Energy Laboratory (NREL) provided the data used in this paper. Index Terms— Discrete wavelet transform, Fault diagnosis, Gearbox Health Condition Monitoring, Support Vector Machine, Vibration Analysis. I. INTRODUCTION The engine consists of various modules like fan, compressor, combustor, turbine and exhaust nozzle. All these modules are connected to the shaft using the engine crucial components such as gears and bearings. Health condition monitoring of these engine components is evolving rapidly as one of the important research fields to get efficient operation of the engine. In this paper, vibration-based fault diagnosis and classification algorithms used for gearbox health condition monitoring. Vibration analysis of gearbox has been done using the Wind Turbine Gearbox Vibration Condition Monitoring Benchmarking Datasets, provided by National Renewable Energy Laboratory (NREL) [1]. II. GEARS- THEORY Gears are the machine components used to change & transmit the rotational motion and torque. In aircraft engines, gears allow the fan as well as the compressor-turbine unit to rotate at different speeds by means of successive engagement of gear teeth. Gear failure happens due to severe stress conditions (When a gear is working under high-stress conditions, there is a tendency to gear failure) [2]. Different types of gears that are used in industries are helical, spur, bevel and worm gears. All these gears have both driven and driving wheels but, differ in shape & wheels arrangement [3].  III. METHODOLOGY Vibration analysis is a valid and most popular condition monitoring technique used for gearbox fault diagnosis [4]. This technique is sensitive to failure and can provide the continuous health state of the gearbox compared to remaining condition monitoring approaches like oil and temperature analysis [5]. Fig. 1 shows the information flow diagram for vibration based gearbox health condition monitoring technique.  A. Data collection The data acquisition device (DAQ) can be used for collecting the vibrations produced by the gears with the help of accelerometers mounted on it. The data used in this paper are obtained from National Renewable Energy Laboratory [1]. Eight accelerometers viz., AN3 to AN10 mounted on the gearbox to sense vibratory acceleration. Sensor location, model and description also mentioned in Ref. [1]. The vibration signal has been acquired at a sampling rate/frequency (Fs) of 40KHz.  After collecting the gearbox data, the signal is processed to diagnose the faults. B. Signal Preprocessing Preprocessing is required to eliminate the artifacts/noise from the raw vibration signal. C. Feature Extraction It is a process/method to obtain meaningful and important characteristics from the signal by transforming it to set of features. Feature extraction can be done in three ways. They are time domain, frequency domain and time-frequency domain analysis. In this paper, a time-frequency domain analysis approach called discrete wavelet transform (DWT) [6, 7] have been used to extract the features.  DWT decompose the signal into a mutually perpendicular set of wavelets and is extensively used for multiresolution signal analysis. Therefore, vibration signal is decomposed into 12 levels and variance of these 12 levels detailed coefficients viz., D1 to D12 and approximation coefficient (A) have been computed.   D. Feature Selection With the thirteen features (A, D1, D2,…, D12) extracted from DWT, a feature vector have been formed to diagnose the gearbox faults.  NCVSPICE2K18, JULY 19-20, 2018  ISBN: 978-93-85100-99-4 Department of ECE, University College of Engineering (A), JNTUK, Kakinada.  Page | 69   E. Fault Classification For classifying the faults, a set of algorithms used such as Support Vector Machines (SVM), Artificial Neural  Networks (ANN), K-Nearest Neighbors (KNN)… etc. SVM have been used for classification in this paper. The feature vector formed by the useful features has been used to train the SVM (Support Vector Machine) classifier [8] for classification of gearbox faults.     Fig. 1. Proposed methodology for gearbox health condition monitoring  IV. RESULTS AND DISCUSSIONS Thirteen features viz., A, D1, D2,…, D12 for each individual sensor from AN3 to AN10 are shown in Fig. 2. It can be observed that the features have larger magnitude for damaged gearbox data than healthy gearbox data. It can also be observed that most of the features have been succeeded in the detection of healthy and damaged conditions. The mean ± standard deviation of these features for all the sensors is shown in Table I.  Fig. 2. Wavelets variance for healthy and damaged gearbox data Fig. 3 shows the gearbox fault classification with SVM classifier using features D11 and D12. It can be observed that healthy and damaged gearbox vectors have been completely overlapped. Hence, misclassification happened and the poor classification region is marked in the circle. Therefore, there is no clear classification between the faults by choosing these features.  Fig. 3. SVM classification using D11 and D12 coefficients Variance The SVM classifier for A and D1 features is shown in Fig. 4. It can be observed that the classifier fails to classify few samples of healthy and damaged gearbox data as marked in Fig. 4. Therefore, these features are moderately classifying the gearbox faults.    Feature Extraction Time domain analysis Frequency domain analysis Time-frequency domain analysis Feature Selection Fault  Diagnosis Gears Accelerometer DAQ Data Pre-processing /  Signal conditioning Vibration Analysis Fault Classification Diagnosis Results Maintenance action Poor Classification NCVSPICE2K18, JULY 19-20, 2018  ISBN: 978-93-85100-99-4 Department of ECE, University College of Engineering (A), JNTUK, Kakinada.  Page | 70   TABLE I.  MEAN ± STANDARD DEVIATIONS OF WAVELETS VARIANCE  Note: Here F represents Features, HG means Healthy Gearbox, DG means Damaged Gearbox and AN3 to AN10 are the sensors mounted on the Gearbox [1].  F AN3 AN4 AN5 AN6 AN7 AN8 AN9 AN10 HG DG HG DG HG DG HG DG HG DG HG DG HG DG HG DG A 0.2275 ± 0.1214 0.7571 ± 0.3824 0.2299 ± 0.1243 0.6552 ± 0.3375 0.5458 ± 0.2459 0.9181 ± 0.5191 1.2065 ± 0.6066 3.3250 ± 1.0959 0.7787 ± 0.4447 2.3191 ± 0.8194 4.9158 ± 2.0870 1.1109 ± 0.6091 2.0361 ± 1.1881 1.9517 ± 1.2719 0.2847 ± 0.1442 1.2531 ± 0.5049 D1 0.1530 ± 0.0080 0.5723 ± 0.0151 0.4275 ± 0.0181 0.7404 ± 0.0317 0.3208 ± 0.0108 1.4172 ± 0.0758 2.5200 ± 0.0525 4.7912 ± 0.3156 3.4853 ± 0.0757 3.4241 ± 0.2272 0.9011 ± 0.0293 9.0570 ± 0.6097 0.0443 ± 0.0060 11.0841 ± 0.5310 0.3938 ± 0.0146 1.1992 ± 0.0415 D2 0.6808 ± 0.0287 3.4323 ± 0.0752 1.8801 ± 0.0798 3.6453 ± 0.2952 1.3506 ± 0.0725 8.5979 ± 0.4233 6.9294 ± 0.1915 15.0876 ± 1.1302 5.6575 ± 0.1219 17.5817 ± 1.0929 5.0031 ± 0.1775 53.2609 ± 3.8804 0.3430 ± 0.0463 54.7211 ± 2.2074 1.7554 ± 0.0756 7.6422 ± 0.2524 D3 2.3822 ± 0.1593 16.225 ± 0.4888 8.0139 ± 0.4783 15.999 ± 1.7525 5.8606 ± 0.4652 30.8988 ± 1.0624 14.815 ± 0.9702 34.3669 ± 2.8056 11.1004 ± 0.5924 38.1653 ± 1.9214 21.0867 ± 1.1031 177.315 ± 15.0063 2.4388 ± 0.3230 128.687 ± 2.8456 6.9473 ± 0.4453 32.7192 ± 1.0713 D4 7.1876 ± 0.5796 50.5071 ± 1.9783 25.168 ± 1.6613 43.9459 ± 2.5689 19.1474 ± 1.6820 81.8606 ± 2.9139 36.2803 ± 3.3440 85.5606 ± 7.8938 26.6282 ± 2.0505 45.8028 ± 2.7604 69.8170 ± 4.0268 263.439 ± 16.6194 13.3740 ± 1.6000 186.443 ± 6.6053 23.7362 ± 1.6087 93.5075 ± 3.3802 D5 5.4685 ± 0.3585 30.3765 ± 1.2644 6.5162 ± 0.5034 37.6879 ± 1.1812 9.8116 ± 0.4630 82.1621 ± 1.9537 11.8499 ± 0.682 55.2967 ± 2.1185 10.9584 ± 0.6496 31.7391 ± 1.3230 114.992 ± 7.8567 108.640 ± 3.2020 44.7519 ± 3.3576 90.4138 ± 3.6987 13.5269 ± 0.8187 82.7891 ± 2.8695 D6 2.8490 ± 0.2853 28.1264 ± 1.2046 8.6963 ± 0.6611 27.3841 ± 1.6290 7.3198 ± 0.6150 60.4370 ± 3.6994 16.7717 ± 1.3269 44.7699 ± 3.6332 11.1465 ± 0.9858 26.9769 ± 1.4512 371.295 ± 41.5093 108.973 ± 6.5468 69.9763 ± 3.5902 116.554 ± 9.4042 9.6438 ± 0.7017 61.3101 ± 3.7262 D7 4.7910 ± 0.5166 39.2156 ± 2.8501 3.3727 ± 0.4596 28.4114 ± 1.8640 2.3291 ± 0.3527 19.9957 ± 1.5611 5.7947 ± 0.6076 13.7286 ± 0.9996 6.5639 ± 1.0968 25.8967 ± 2.3241 529.232 ± 58.7680 59.5544 ± 5.7913 39.9421 ± 4.4502 57.8479 ± 12.5309 5.2185 ± 0.5030 20.5704 ± 1.6354 D8 5.9372 ± 0.6477 25.1100 ± 2.3644 3.5311 ± 0.4906 19.6788 ± 1.6970 3.6852 ± 0.3822 12.3970 ± 1.8432 5.1140 ± 0.5725 12.9348 ± 1.7589 7.5278 ± 1.1918 18.2374 ± 1.8651 45.2877 ± 3.8284 36.1166 ± 4.0360 7.7666 ± 0.9861 24.4225 ± 2.6621 4.0901 ± 0.7088 18.1544 ± 3.1592 D9 3.4685 ± 0.6162 12.5119 ± 1.8736 5.9497 ± 1.3855 10.2976 ± 1.5901 14.0785 ± 1.1688 15.3568 ± 2.8836 16.9593 ± 1.9558 38.7317 ± 7.4553 10.2570 ± 1.1834 30.8680 ± 4.5936 38.4616 ± 5.0313 19.1569 ± 3.4943 17.3802 ± 2.2836 23.2279 ± 3.6959 7.7023 ± 1.2823 20.2729 ± 3.9366 D10 5.0998 ± 0.8017 10.6367 ± 2.6197 16.5956 ± 3.3660 10.7474 ± 2.4219 42.4103 ± 2.7762 25.6327 ± 4.6040 44.2906 ± 4.6885 81.9085 ± 17.4621 29.3453 ± 2.8065 64.0179 ± 10.4165 76.2027 ± 10.6621 20.0914 ± 4.5095 39.6413 ± 4.2309 36.2096 ± 4.7162 21.9692 ± 3.1299 41.5993 ± 8.0105 D11 1.5110 ± 0.4298 4.5727 ± 1.3597 2.3081 ± 0.5219 3.6679 ± 1.0979 7.4643 ± 1.4039 5.1696 ± 1.2731 9.1331 ± 2.1894 3.6855 ± 1.0877 7.2221 ± 2.0287 5.6761 ± 1.4694 14.7461 ± 4.5470 7.1663 ± 2.3357 11.3958 ± 3.0974 8.2808 ± 2.2511 3.1100 ± 0.6556 4.1992 ± 1.1306 D12 1.1058 ± 0.4988 1.9319 ± 0.8605 3.8066 ± 1.5930 1.7707 ± 0.7777 11.2065 ± 4.3012 5.7995 ± 2.3505 11.7018 ± 4.8414 4.3114 ± 1.8650 8.0789 ± 3.2624 6.0659 ± 2.3060 13.8821 ± 5.2143 4.4268 ± 1.9557 12.1913 ± 5.1523 9.4717 ± 3.9200 5.2146 ± 2.0484 6.3469 ± 2.8520 NCVSPICE2K18, JULY 19-20, 2018  ISBN: 978-93-85100-99-4 Department of ECE, University College of Engineering (A), JNTUK, Kakinada.  Page | 71     Fig. 4. SVM classification using A and D1 coefficients variance SVM classification using features D9 and D10 is shown in Fig.6. It can be observed that both healthy and damaged gearbox data are clearly classified using these features.   Fig. 5. SVM classification using D9 and D10 coefficients variance Table II represents all the features with their usefulness in classifying the gearbox faults. The features marked with green color are very useful for gearbox fault classification using SVM classifier. Similarly, the features that are shown with yellow color are marginally classifying the faults. Remaining wavelet features that are shown with white color in Table II are not useful features for fault classification.  TABLE II.  USEFULNESS OF FEATURES Features Wavelets Variance AN3 AN4 AN5 AN6 AN7 AN8 AN9 AN10 A        D1        D2        D3        D4        D5        D6        D7        D8        D9        D10        D11        D12        Note: Here,  represents clear or perfect classification,  moderate classification and  represents very poor classification. V. CONCLUSION This paper deals with vibration analysis based gearbox health condition monitoring. DWT had been used to extract the wavelet variance features from the vibration data. SVM classifier had been used to classify healthy and damaged data. The useful features for gearbox fault diagnosis had been presented. The results reveal that the vibration based condition monitoring technique is successful in diagnosing and classifying the gearbox faults. This condition monitoring algorithm is simple and can be used for real time applications as well. ACKNOWLEDGMENT The authors would like to acknowledge National Renewable Energy Laboratory (NREL) for providing the Wind Turbine Gearbox Vibration Condition Monitoring Benchmarking Datasets.  REFERENCES [1] https://openei.org/datasets/dataset/wind-turbine-gearbox-condition-monitoring-vibration-analysis-benchmarking-datasets, Shuangwen Sheng, “Wind Turbine Gearbox Vibration Condition Monitoring Benchmarking Datasets”, National Renewable Energy Laboratory, USA. [2] S. Devendiran and Dr. K. Manivannan, “Vibration Based Condition Monitoring and Fault Diagnosis Technologies for Bearing and Gear Components-A Review”, International Journal of Applied Engineering Research, Volume 11, Number 6 (2016) pp 3966-3975. [3] Al-Arbi, Salem, “Condition Monitoring of Gear Systems using Vibration Analysis”, Doctoral Thesis, University of Huddersfield, 2012. [4] Pramod Bhatia, “Condition monitoring of gear boxes in real time”, International Journal of Current Research, Vol. 8, Issue, 06, pp.33313-33321, June 2016.  [5] P. Večeř, M. Kreidl and R. Šmíd, “Condition Indicators for Gearbox Condition Monitoring Systems”, Czech Technical University in Prague Acta Polytechnica, Vol. 45 No. 6/2005. [6] V. Shanmukha Priya, P. Mahalakshmi, and VPS Naidu, “Bearing Health Condition Monitoring: Wavelet Decomposition”, Indian Journal of Science and Technology, Vol 8(26), IPL0569, October 2015. [7] Rajani J. and VPS Naidu, “Bearing Fault Diagnosis using DWT & SVM”, National Conference on Electronics, Signals and Communication” (NCESC-2018), Paper ID: NCESC18-80, Organized by GSSS IETW, Mysore, 17 May 2018. [8] Shanmukha Priya V., M. R. Ramesh, and VPS Naidu, “Bearing fault Classification using Support Vector Machines” Paper No. T01, pp.1-8, ASI-HMFD 2015, organized by VSSC, Trivandrum, May 22-23, 2015.       Misclassification 

Gearbox Health Condition Monitoring: Vibration

Analysis

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Gearbox Health Condition Monitoring: Vibration Analysis

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