Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2022, Vol. 62 Issue (9): 1458-1466    DOI: 10.16511/j.cnki.qhdxxb.2022.21.014
  过程系统工程 本期目录 | 过刊浏览 | 高级检索 |
基于破坏性实验和自动编码器的风机皮带剩余寿命预测
赵泽恒, 赵劲松
清华大学 化学工程系, 北京 100084
Remaining useful life prediction of fan belts based on destructive experiments and autoencoders
ZHAO Zeheng, ZHAO Jinsong
Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
全文: PDF(7577 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 智能化的风机皮带寿命预测系统有助于确保化工安全,同时减少人工劳力。该文提取振动信号的特征,输入多层自动编码器得到健康指标(HI),通过多项式拟合HI即可预测剩余寿命。为缩短数据采集周期,开展了破坏性实验,并构建了适合破坏性实验的损失函数。同时,基于多层自动编码器的结构进行了改进,将中间层的内积与激活函数运算分离计算,限定HI为[0,1]的同时保留更多编码层信息。与传统自动编码器方法相比,中间层分离的自动编码器在实际问题中降低了预测误差,实现了3组数据后50%区段预测误差不超过1 d的良好结果,上线测试同样表现良好。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
赵泽恒
赵劲松
关键词 故障诊断自动编码器剩余寿命预测    
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.
Key wordsfault diagnosis    autoencoder    remaining useful life prediction
收稿日期: 2022-01-18      出版日期: 2022-08-18
基金资助:赵劲松,教授,E-mail:jinsongzhao@tsinghua.edu.cn
引用本文:   
赵泽恒, 赵劲松. 基于破坏性实验和自动编码器的风机皮带剩余寿命预测[J]. 清华大学学报(自然科学版), 2022, 62(9): 1458-1466.
ZHAO Zeheng, ZHAO Jinsong. Remaining useful life prediction of fan belts based on destructive experiments and autoencoders. Journal of Tsinghua University(Science and Technology), 2022, 62(9): 1458-1466.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2022.21.014  或          http://jst.tsinghuajournals.com/CN/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.
[1] 雷政, 姜鹏, 王启明. FAST促动器故障预测与健康管理系统[J]. 清华大学学报(自然科学版), 2022, 62(11): 1796-1802.
[2] 杨宏宇, 王峰岩, 吕伟力. 基于无监督生成推理的网络安全威胁态势评估方法[J]. 清华大学学报(自然科学版), 2020, 60(6): 474-484.
[3] 宋佳, 石若凌, 郭小红, 刘杨. 基于核极限学习机的飞行器故障诊断方法[J]. 清华大学学报(自然科学版), 2020, 60(10): 795-803.
[4] 董春玲, 赵越, 张勤. 动态故障诊断中的立体因果建模与不确定性推理方法[J]. 清华大学学报(自然科学版), 2018, 58(7): 614-622.
[5] 吐松江·卡日, 高文胜, 张紫薇, 莫文雄, 王红斌, 崔屹平. 基于支持向量机和遗传算法的变压器故障诊断[J]. 清华大学学报(自然科学版), 2018, 58(7): 623-629.
[6] 杨倩文, 孙富春. 基于泛化空间正则自动编码器的遥感图像识别[J]. 清华大学学报(自然科学版), 2018, 58(2): 113-121.
[7] 赵越, 董春玲, 张勤. 动态不确定因果图用于复杂系统故障诊断[J]. 清华大学学报(自然科学版), 2016, 56(5): 530-537,543.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn