Ensemble Learning based Model for Multi-Sensor Vibration Data Fusion in Gearbox Diagnosis

Document Type : Research Article

Authors

1 Department of Mechanical Engineering, University of Zanjan, Zanjan, Iran

2 Master of Mechanical Engineering, Department of Mechanical Engineering, University of Zanjan, Zanjan, Iran

Abstract

This study investigates recorded vibration signals from a laboratory gearbox to assess health condition and identify fault types, using a proposed ensemble-based machine learning algorithm. A single-stage gearbox was designed and tested in laboratory under four healththe  states: no faults, tooth root crack, tooth breakage, and pitting on the tooth, across varying loads and speeds. Vibration was recorded at six points. Totally 792 signals (6 signals from 132 tests) were collected. For the data from each sensor, a support vector machine (SVM) classifier with a linear kernel was trained. Next, fault detection accuracy was assessed and compared for each transducer individually. A new data fusion algorithm, inspired by random forest (RF), was developed to combine data from the six sensors. The results showed that the proposed ensemble algorithm provides higher detection accuracy rather than the individual classifiers for each sensor. In addition, a novel method is introduced to estimate the confidence level (CL) of the classification by the proposed algorithm. In addition, it is demonstrated that the proposed algorithm can effectively diagnose faults with incomplete data (regardless of how many sensors are used from the total of six). As expected, using data from fewer sensors resulted in reduced accuracy and CL.

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Main Subjects

References

[1] P. Goswami, R.N. Rai, A systematic review on failure modes and proposed methodology to artificially seed faults for promoting PHM studies in laboratory environment for an industrial gearbox, Engineering Failure Analysis, 146 (2023) 107076.
[2] P. Ku, Gear failure modes—importance of lubrication and mechanics, ASLe Transactions, 19(3) (1976) 239-249.
[3] R.B. Randall, Vibration-based condition monitoring: industrial, automotive and aerospace applications, John Wiley & Sons, 2021.
[4] Y. Zhao, X. Wang, S. Han, J. Lin, Q. Han, Fault diagnosis for abnormal wear of rolling element bearing fusing oil debris monitoring, Sensors, 23(7) (2023) 3402.
[5] A.R. Mohanty, C. Kar, Fault detection in a multistage gearbox by demodulation of motor current waveform, IEEE transactions on Industrial Electronics, 53(4) (2006) 1285-1297.
[6] A.M. Younus, B.-S. Yang, Intelligent fault diagnosis of rotating machinery using infrared thermal image, Expert Systems with Applications, 39(2) (2012) 2082-2091.
[7] X. Lu, P. Li, Research on gearbox temperature field image fault diagnosis method based on transfer learning and deep belief network, Scientific Reports, 13(1) (2023) 6664.
[8] V. Singh, P. Gangsar, R. Porwal, A. Atulkar, Artificial intelligence application in fault diagnostics of rotating industrial machines: A state-of-the-art review, Journal of Intelligent Manufacturing, 34(3) (2023) 931-960.
[9] Z. Tian, M.J. Zuo, Health condition prediction of gears using a recurrent neural network approach, IEEE transactions on reliability, 59(4) (2010) 700-705.
[10] P. Calefati, B. Amico, A. Lacasella, E. Muraca, M.J. Zuo, Machinery faults detection and forecasting using hidden Markov models, in:  Engineering Systems Design and Analysis, 2006, pp. 895-901.
[11] B.S. Yang, C.H. Park, H.J. Kim, An efficient method of vibration diagnostics for rotating machinery using a decision tree, International Journal of Rotating Machinery, 6(1) (2000) 19-27.
[12] I. Jamadar, R. Nithin, S. Nagashree, V.P. Prasad, M. Preetham, P. Samal, S. Singh, Spur Gear Fault Detection Using Design of Experiments and Support Vector Machine (SVM) Algorithm, Journal of Failure Analysis and Prevention, 23(5) (2023) 2014-2028.
[13] B. Samanta, Gear fault detection using artificial neural networks and support vector machines with genetic algorithms, Mechanical systems and signal processing, 18(3) (2004) 625-644.
[14] N. Saravanan, K. Ramachandran, Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN), Expert systems with applications, 37(6) (2010) 4168-4181.
[15] Z. Chen, C. Li, R.-V. Sanchez, Gearbox fault identification and classification with convolutional neural networks, Shock and Vibration, 2015(1) (2015) 390134.
[16] P. Večeř, M. Kreidl, R. Šmíd, Condition indicators for gearbox condition monitoring systems, Acta Polytechnica, 45(6) (2005).
[17] V. Sharma, A. Parey, A review of gear fault diagnosis using various condition indicators, Procedia Engineering, 144 (2016) 253-263.
[18] H. Ahmed, A.K. Nandi, Condition monitoring with vibration signals: Compressive sampling and learning algorithms for rotating machines, John Wiley & Sons, 2020.
[19] E. Bechhoefer, M. Kingsley, A review of time synchronous average algorithms, in:  Annual Conference of the PHM society, 2009.
[20] W.I.D. Mining, Introduction to data mining, Springer, 2006.
Amirkabir Journal of Mechanical Engineering
Volume 56, Issue 10
2025
Pages 1329-1354

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