Intelligent Fault Diagnosis of Synchromesh Gearbox Using Fusion of Vibration and Acoustic Emission Signals for Performance Enhancement
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Published
Jun 1, 2019
T Praveenkumar
M Saimurugan
K I Ramachandran
Abstract
Condition monitoring system monitors the system degradation and it identifies common failure modes. Several sensor signals are available for monitoring the changes in system components. Vibration signal is one of the most extensively used technique for monitoring rotating components as it identifies faults before the system fails. Early fault detection is the significant factor for condition monitoring, where Acoustic Emission ( AE ) sensor signals have been applied for early fault detection due to their high sensitivity and high frequency. In this paper, vibration and acoustic emission signals are acquired under various simulated gear and bearing fault conditions from the synchromesh gearbox. Then the statistical features are extracted from vibration and AE signals and then the prominent features are selected using J48 decision tree algorithm respectively. The best features from the vibration and AE signals are then fused using feature-level fusion strategy and it is classified using Support Vector Machine ( SVM ) and Proximal Support Vector Machine ( PSVM ) classifiers and it is compared with individual signals for fault diagnosis of the synchromesh gearbox. From the experiments, it is observed that the performance of the fault diagnosis system has been improved for the proposed feature level fusion technique compared to the performance of unfused vibration and AE feature sets.
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Keywords
Fault diagnosis, Vibration signal, Acoustic Emission signal, Feature level fusion
References
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AshkanMoosavian, MeghdadKhazaee, GholamhassanNajafi, Maurice Kettner&RizalmanMamat.(2015). Spark plug fault recognition based on sensor fusion and classifier combination using dempster–Shafer evidence theory.Applied Acoustics, vol. 93, pp. 120-129.doi: 10.1016/j.apacoust.2015.01.008
BostjanDolenc, PavleBoskoski&DaniJurici. (2016). Distributed bearing fault diagnosis based on vibration analysis. Mechanical Systems and Signal Processing, vol. 66-67, pp. 521-532.doi: 10.1016/j.ymssp.2015.06.007
Bo-Suk Yang &Kwang Jin Kim.(2006). Application of dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals.Mechanical Systems and Signal Processing, vol. 20, pp. 403-420.doi: 10.1016/j.ymssp.2004.10.010
David Logan&Joseph Mathew. (1996). Using the correlation dimension for vibration fault diagnosis of rolling element bearings—I. basic concepts. Mechanical Systems and Signal Processing, vol. 10(3), pp. 241-250.doi: 10.1006/mssp.1996.0018
FarisElasha, Matthew Greaves, David Mba&AbdulmajidAddali. (2015). Application of acoustic emission in diagnostic of bearing faults within a helicopter gearbox.Procedia CIRP, vol. 38, pp. 30-36.doi:
Gang Niu, Tian Han, Bo-Suk Yang & Andy Chit Chiow Tan. (2007). Multi-agent decision fusion for motor fault diagnosis.Mechanical Systems and Signal Processing, vol. 21, pp. 1285-1299.doi: 10.1016/j.procir.2015.08.042
Hong Fei Wang&Jiang Ping Wang. (2000). Fault diagnosis theory: method and application based on multisensor data fusion. Journal of Testing and Evaluation, vol. 28(6), pp. 513-518.doi: 10.1520/JTE12143J.
Hung-Chih Chiang, Randolph L. Moses & Lee C. Potter. (2001). Model- based Bayesian feature matching with application to synthetic aperture radar target recognition. Pattern Recognition, vol. 34, pp. 1539-1553.doi: 10.1016/S0031-3203(00)00089-3
Jian Yang, Jing-Yu Yang, David Zhang &Jian-Feng Lu. (2003). Feature fusion: parallel strategy vs. serial strategy. Pattern Recognition, vol. 36, pp. 1369-1381.doi: 10.1016/S0031-3203(02)00262-5
Junyan Yang, Youyun Zhang & Yong Sheng Zhu.(2007). Intelligent fault diagnosis of rolling element bearing based on SVMS and fractal dimension.Mechanical Systems and Signal Processing, vol. 21, pp. 2012-2024.doi: 10.1016/j.ymssp.2006.10.005
Kuan Fang He, Jigang Wu &Guang Bin Wang. (2012). Acoustic emission signal feature extraction in rotor crack fault diagnosis. Journal of Computers, vol. 7(9), pp. 2120-2127.
Lei, Y., Lin, J., He, Z., &Zuo, M. J. (2013).A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, vol. 35, pp. 108-126.doi: 10.1016/j.ymssp.2012.09.015
Li, N., Zhou, R., Hu, Q., & Liu, X. (2012). Mechanical fault diagnosis based on redundant second-generation wavelet packet transform, neighborhood roughest and support vector machine, Mechanical Systems and Signal Processing, vol. 28, pp. 608-621. DOI: 10.1016/j.ymssp.2011.10.016
Loutas, T. H., Roulias, D., Pauly, E. &Kostopoulos, V. (2011).The combined use of vibration, acoustic emission, and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mechanical Systems and Signal Processing, vol. 25, pp. 1339-1352.doi: 10.1016/j.ymssp.2010.11.007
MeghdadKhazaee, HojatAhmadi, Mahmoud Omid, AshkanMoosavian&MajidKhazaee.(2014). Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on dempster–Shafer evidence theory.ProcIMech E Part E: J Process Mechanical Engineering, vol. 228(1), pp. 21-32.doi: 10.1177/0954408912469902
OtmanBasir& Xiao hong Yuan.(2007). Engine fault diagnosis based on multi-sensor information fusion using dempster–Shafer evidence theory.Information Fusion, vol. 8, pp. 379-386.doi: 10.1016/j.inffus.2005.07.003
Praveenkumar, T., Sabhrish, B., Saimurugan, M., &Ramachandran, K.I. (2018).Pattern recognition based on-line vibration monitoring system for fault diagnosis of automobile gearbox. Measurement, vol. 114, 233-242.doi: 10.1016/j.measurement.2017.09.041
Rai, V. K., &Mohanty, A. R. (2007). Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform. Mechanical Systems and Signal Processing, vol. 21(8), pp. 3030-3041.doi: 10.1016/j.ymssp.2006.12.004
Rubini, R. &Meneghetti, U. (2001). Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing, vol. 15, pp. 287-302.doi: 10.1006/mssp.2000.1330
Chen, W., (1991). Nonlinear Analysis of Electronic Prognostics.Doctoral dissertation.The Technical University of Napoli, Napoli, Italy.
Ruoyu Li., (2012). Rotating Machine Fault Diagnostics using Vibration and Acoustic Emission Sensors. Doctoral dissertation.UIC Graduate College - University of Illinois, Chicago.
Safizadeh, M. S., &Latifi, S. K. (2014).Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell.Information Fusion, vol. 18, pp. 1-8.doi: 10.1016/j.inffus.2013.10.002
Saimurugan, M., &Nithesh, R. (2016). Intelligent fault diagnosis model for rotating machinery based on fusion of sound signals, International Journal of Prognostics And Health Management, pp. 1-10.
Saimurugan, M., Ramachandran, K. I., Sugumaran, V., &Sakthivel, N. R. (2011).Multi-component fault diagnosis of rotational mechanical system based on decision tree and support vector machine.Expert Systems with Applications, vol. 38, pp. 3819-3826.doi:10.1016/j.eswa.2010.09.042
Saimurugan, M., & Ramprasad, R. (2017). A dual-sensor signal fusion approach for detection of faults in rotating machines. Journal of Vibration and Control, pp. 2621-2630.doi:10.1177/1077546316689644
Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2003). Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, vol. 16, pp. 657-665.doi:10.1016/j.engappai.2003.09.006
Sanjay Taneja. (2013). Effect of unbalance on performance of centrifugal pump.International Journal of Scientific & Technology Research, vol. 2(8), pp. 56-60.
Saravanan, N., Kumar Siddabattuni, V. N. S., &Ramachandran, K. I. (2010). Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM). Applied Soft Computing, vol. 10,pp.344-360.doi:10.1016/j.asoc.2009.08.006
Sultan Binsaeid, ShihabAsfour, Sohyung Cho &ArzuOnar.(2009). Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion.Journal of Materials Processing Technology, vol. 209, pp. 4728-4738.doi:10.1016/j.jmatprotec.2008.11.038
Wei Li, Zhencai Zhu, Fan Jiang, Gong Bo Zhou &Guoan Chen. (2015). Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method.Mechanical Systems and Signal Processing, vol. 50-51, pp. 414-426.doi:10.1016/j.ymssp.2014.05.034
Xiao Li Zhang, BaoJian Wang &XueFeng Chen. (2015). Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine.Knowledge-Based Systems, vol. 89, pp. 56-85.doi:
Yang, Y., Yu, D. J., & Cheng, J. S. (2007). A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM.Measurement, vol. 40(9–10), pp. 943-950.doi:10.1016/j.knosys.2015.06.017
YongzhiQu, David He, Jae Yoon, Brandon Van Hecke, Eric Bechhoefer&Junda Zhu. (2014). Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors — a comparative study. Sensors, vol. 14, pp. 1372-1393.doi: 10.3390/s140101372.
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Technical Papers