Remaining useful life prediction of fan belts based on destructive experiments and autoencoders

doi:10.16511/j.cnki.qhdxxb.2022.21.014

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Abstract A smart system predicting the remaining useful life (RUL) of fan belts can ensure the safety of chemical engineering processes and reduce human labor. In this study, the features extracted from the vibration signals of the belt are fed into a stacked autoencoder (SAE) to obtain the health indicator (HI). Then, a polynomial is fitted using the HI to calculate the RUL of the belt. The destructive experiment is conducted to shorten the data collection period, and a loss function is constructed to fit the destructive experiment. This study also improved the structure of the SAE, i.e., the inner product and the activation operation of the middle layer are separately done in two neurons. This bounds the HI in [0,1] and preserves more information from the encoding layers. Compared with traditional SAEs, the proposed method reduces the prediction errors in real cases. The errors of the predicted RUL are bounded by 1 d in the last 50% of the sections of all three datasets. The trained model also performs well when employed on an online test.

Keywords fault diagnosis autoencoder remaining useful life prediction

Issue Date: 18 August 2022

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	ZHAO Zeheng
	ZHAO Jinsong

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ZHAO Zeheng,ZHAO Jinsong. Remaining useful life prediction of fan belts based on destructive experiments and autoencoders[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(9): 1458-1466.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2022.21.014 OR http://jst.tsinghuajournals.com/EN/Y2022/V62/I9/1458

[1] LI W B, XIN Z X. Flexural fatigue life prediction of a tooth V-belt made of fiber reinforced rubber[J]. International Journal of Fatigue, 2018, 111: 269-277. DOI: 10.1016/j.ijfatigue.2018.02.025.
[2] SUNDARARAMAN S, LIANG G, CHANDRASHEKHARA K, et al. Mode-I fatigue crack growth analysis of V-ribbed belts[J]. Finite Elements in Analysis and Design, 2007, 43(11-12): 870-878. DOI: 10.1016/j.finel.2007.05.003.
[3] SAFIZADEH M S, LATIFI S K. Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell[J]. Information Fusion, 2014, 18: 1-8. DOI: 10.1016/j.inffus.2013.10.002.
[4] CONG F Y, CHEN J, DONG G M, et al. Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis[J]. Journal of Sound and Vibration, 2013, 332(8): 2081-2097. DOI: 10.1016/j.jsv.2012.11.029.
[5] GLOWACZ A, GLOWACZ W, GLOWACZ Z, et al. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals[J]. Measurement, 2018, 113: 1-9. DOI: 10.1016/j.measurement.2017.08.036.
[6] LEI Y G, LI N P, GUO L, et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104: 799-834. DOI: 10.1016/j.ymssp.2017.11.016.
[7] KHAZAEE M, BANAKAR A, GHOBADIAN B, et al. Remaining useful life (RUL) prediction of internal combustion engine timing belt based on vibration signals and artificial neural network[J]. Neural Computing and Applications, 2021, 33(13): 7785-7801. DOI: 10.1007/s00521-020-05520-3.
[8] QIU H, LEE J, LIN J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006, 289(4-5): 1066-1090. DOI: 10.1016/j.jsv.2005.03.007.
[9] VILLA L F, RE?ONES A, PER?N J R, et al. Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load[J]. Mechanical Systems and Signal Processing, 2012, 29: 436-446. DOI: 10.1016/j.ymssp.2011.12.013.
[10] SCHIJVE J. Four lectures on fatigue crack growth[J]. Engineering Fracture Mechanics, 1979, 11(1): 169-181. DOI: 10.1016/0013-7944(79)90039-0.
[11] BENKEDJOUH T, MEDJAHER K, ZERHOUNI N, et al. Remaining useful life estimation based on nonlinear feature reduction and support vector regression[J]. Engineering Applications of Artificial Intelligence, 2013, 26(7): 1751-1760. DOI: 10.1016/j.engappai.2013.02.006.
[12] IBRAHIM M, STEINER N Y, JEMEI S, et al. Wavelet-based approach for online fuel cell remaining useful lifetime prediction[J]. IEEE Transactions on Industrial Electronics, 2016, 63(8): 5057-5068. DOI: 10.1109/tie.2016.2547358.
[13] CHEN C, XU T H, WANG G, et al. Railway turnout system RUL prediction based on feature fusion and genetic programming[J]. Measurement, 2020, 151: 107162. DOI: 10.1016/j.measurement.2019.107162.
[14] YAN B X, MA X B, HUANG G F, et al. Two-stage physics-based Wiener process models for online RUL prediction in field vibration data[J]. Mechanical Systems and Signal Processing, 2021, 152: 107378. DOI: 10.1016/j.ymssp.2020.107378.
[15] BARRAZA-BARRAZA D, TERCERO-G?MEZ V G, BERUVIDES M G, et al. An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth[J]. Mechanical Systems and Signal Processing, 2017, 82: 519-536. DOI: 10.1016/j.ymssp.2016.05.041.
[16] TANDON N. A comparison of some vibration parameters for the condition monitoring of rolling element bearings[J]. Measurement, 1994, 12(3): 285-289. DOI: 10.1016/0263-2241(94)90033-7.
[17] YADAV S K, TYAGI K, SHAH B, et al. Audio signature-based condition monitoring of internal combustion engine using FFT and correlation approach[J]. IEEE Transactions on Instrumentation and Measurement, 2011, 60(4): 1217-1226. DOI: 10.1109/tim.2010.2082750.
[18] ZHANG Z X, SI X S, HU C H. An age-and state-dependent nonlinear prognostic model for degrading systems[J]. IEEE Transactions on Reliability, 2015, 64(4): 1214-1228. DOI: 10.1109/tr.2015.2419220.
[19] RAI A, KIM J M. A novel health indicator based on the Lyapunov exponent, a probabilistic self-organizing map, and the Gini-Simpson index for calculating the RUL of bearings[J]. Measurement, 2020, 164: 108002. DOI: 10.1016/j.measurement.2020.108002.
[20] CHEN D L, QIN Y, WANG Y, et al. Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction[J]. ISA Transactions, 2021, 114: 44-56. DOI: 10.1016/j.isatra.2020.12.052.
[21] QIAN Y N, YAN R Q, HU S J. Bearing degradation evaluation using recurrence quantification analysis and Kalman filter[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(11): 2599-2610. DOI: 10.1109/tim.2014.2313034.
[22] SHE D M, JIA M P, PECHT M G. Sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing[J]. Measurement Science and Technology, 2020, 31(10): 105005. DOI: 10.1088/1361-6501/ab8c0f.
[23] SHEN C Q, QI Y M, WANG J, et al. An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder[J]. Engineering Applications of Artificial Intelligence, 2018, 76: 170-184. DOI: 10.1016/j.engappai.2018.09.010.

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