Journal of Mechanical Engineering >

2018 , Vol. 54 >Issue 5: 94 - 104

DOI: https://doi.org/10.3901/JME.2018.05.094

Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era

  • LEI Yaguo ,
  • JIA Feng ,
  • KONG Detong ,
  • LIN Jing ,
  • XING Saibo
Expand
  • 1. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049;
    2. Huadian Electric Power Research Institute, Hangzhou 310030

Received date: 2016-12-16

  Revised date: 2017-08-17

  Online published: 2018-03-05

Fold

Abstract

Faults are a potential killer of large-scale mechanical equipment, such as wind power equipment, aircraft engines and high-end CNC machine. And fault diagnosis plays an irreplaceable role in ensuring the health operation of such equipment. Since the amount of the equipment diagnosed is great and the number of the sensors for the equipment is large, massive data are acquired by the high sampling frequency after the long-time operation of equipment. Such massive data promote fault diagnosis to enter the era of big data. And machinery intelligent fault diagnosis is a promising tool to deal with mechanical big data. In the big data era, new opportunities have been brought to intelligent fault diagnosis. For instance, data-centric academic thinking will become mainstream, it makes fault diagnosis in the system level possible, and a comprehensive analysis of faults becomes a trend. Meanwhile, new challenges have also been brought:the data are big but fragmentary, the fault feature extraction relies on much prior knowledge and diagnostics expertise, and the generalization ability of the shallow diagnosis model is weak. The characteristics of big data in intelligent fault diagnosis are discussed, and the inland and overseas research advances are reviewed from the three steps of intelligent fault diagnosis. The existing key problems of the current research in the era of big data are pointed out, and the approaches and research directions to these problems are discussed in the end.

Key words： mechanical equipment; intelligent fault diagnosis; big data

Cite this article

LEI Yaguo , JIA Feng , KONG Detong , LIN Jing , XING Saibo . Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era[J]. Journal of Mechanical Engineering, 2018 , 54(5) : 94 -104 . DOI: 10.3901/JME.2018.05.094

References

[1] LEI Yaguo,JIA Feng,LIN Jing,et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J]. IEEE Transactions on Industrial Electronics,2016,63(5):3137-3147.
[2] 李国杰,程学旗. 大数据研究:未来科技及经济社会发展的重大战略领域-大数据的研究现状与科学思考[J]. 中国科学院院刊,2012,27(6):647-657. LI Guojie,CHEN Xueqi. Research status and scientific thinking of big data[J]. Bulletin of the Chinese Academy of Sciences,2012,27(6):647-657.
[3] QIN S J. Process data analytics in the era of big data[J]. AIChE Journal,2014,60(9):3092-3100.
[4] 刘智慧,张泉灵. 大数据技术研究综述[J]. 浙江大学学报,2014,48(6):957-972. LIU Zhihui,ZHANG Quanling. Research overview of big data technology[J]. Journal of Zhejiang University,2014,48(6):957-972.
[5] 郎杨琴,孔丽华. 美国发布"大数据的研究和发展计划"[J]. 科研信息化技术与应用,2012,3(2):89-93. LANG Yangqin,KONG Lihua. The U.S. government released "Big data research and development initiative"[J]. e-Science Technology & Application,2012,3(2):89-93.
[6] 工业和信息化部信息电信研究院. 工信部电信研究院大数据白皮书[EB/OL].[2014-05-12]. http://www.cctime.com/html/2014-5-12/20145121139179652.htm. China Academy of Telecommunication Research of MIT. White paper on big data[EB/OL].[2014-05-12]. http://www.cctime.com/html/2014-5-12/20145121139179652.htm.
[7] 中华人民共和国国务院. 促进大数据发展行动纲要[EB/OL].[2015-11-16]. http://www.gov.cn/zhengce/content/2015-09/05/content_10137.htm. State Council of the People's Republic of China. Action plan for promoting the big data development[EB/OL].[2015-11-16]. http://www.gov.cn/zhengce/content/2015-09/05/content_10137.htm.
[8] EVANS P C,ANNUNZIATA M. Industrial internet:Pushing the boundaries of minds and machines[J]. Sci. Rep. Kanazawa Univ.,2012(1-2):1-23.
[9] 雷亚国,贾峰,周昕,等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报,2015,51(21):49-56. LEI Yaguo,JIA Feng,ZHOU Xin,et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering,2015,51(21):49-56.
[10] 雷亚国,何正嘉. 混合智能故障诊断与预示技术的应用进展[J]. 振动与冲击,2011,30(9):129-135. LEI Yaguo,HE Zhengjia. Advances in applications of hybrid intelligent fault diagnosis and prognosis technique[J]. Journal of Vibration and Shock,2011,30(9):129-135.
[11] 王国彪,何正嘉,陈雪峰,等. 机械故障诊断基础研究"何去何从"[J]. 机械工程学报,2013,49(1):63-72. WANG Guobiao,HE Zhengjia,CHEN Xuefeng,et al. Basic research on machinery fault diagnosis-What is the prescription[J]. Journal of Mechanical Engineering,2013,49(1):63-72.
[12] TUMER I Y,HUFF E M. Using triaxial accelerometer data for vibration monitoring of helicopter gearboxes[C]//Proceedings of the ASME Mechanical Vibration and Noise Conference,2001.
[13] SAPONARA S,CASAROSA G,HAMBLOCH P,et al. Modeling,sensitivity analysis,and prototyping of low-g acceleration acquisition systems for spacecraft testing and environmental-noise measurements[J]. IEEE Transactions on Instrumentation and Measurement,2011,60(2):385-397.
[14] SINHA J K,ELBHBAH K. A future possibility of vibration based condition monitoring of rotating machines[J]. Mechanical Systems and Signal Processing,2013,34(1):231-240.
[15] LEI Yaguo,LIU Zongyao,WU Xionghui,et al. Health condition identification of multi-stage planetary gearboxes using a mRVM-based method[J]. Mechanical Systems and Signal Processing,2015,60:289-300.
[16] 王荣,贾民平,刘桂兴. 状态监测振动传感器优化布置理论及应用[J]. 东南大学学报,2011,41(1):77-81. WANG Rong,JIA Minping,LIU Guixing. Theory and application of sensor placement in condition monitoring[J]. Journal of Southeast University,2011,41(1):77-81.
[17] 汤宝平,黄庆卿,邓蕾,等. 机械设备状态监测无线传感器网络研究进展[J]. 振动测试与诊断,2014,34(1):1-7. TANG Baoping,HUANG Qingqing,DENG Lei,et al. Research progess and challenges of wireless sensor networks for machinery equipment condition monitoring[J]. Journal of Vibration,Measurement & Diagnosis,2014,34(1):1-7.
[18] AIDA R,AZZEDINE D,VIRESH W,et al. A comparison study between acoustic sensors for bearing fault detection under different speed and load using a variety of signal processing techniques[J]. Tribology Transactions,2011,54(2):179-186.
[19] SCANLON P,KAVANAGH D F,BOLAND F M. Residual life prediction of rotating machines using acoustic noise signals[J]. IEEE Transactions on Instrumentation and Measurement,2013,62(1):95-108.
[20] GLOWACZ A. Diagnostics of DC and induction motors based on the analysis of acoustic signals[J]. Measurement Science Review,2014,14(5):257-262.
[21] ZHANG Xiaozheng,BI Chuanxing,ZHANG Yongbin,et al. Real-time nearfield acoustic holography for reconstructing the instantaneous surface normal velocity[J]. Mechanical Systems and Signal Processing,2015,52:663-671.
[22] ZHANG Haibin,ZHANG Shangbin,HE Qingbo,et al. The Doppler effect based acoustic source separation for a wayside train bearing monitoring system[J]. Journal of Sound and Vibration,2016,361:307-329.
[23] GU D,KIM J,KELIMU T,et al. Evaluation of the use of envelope analysis and DWT on AE signals generated from degrading shafts[J]. Materials Science and Engineering:B,2012,177(19):1683-1690.
[24] NIKNAM S A,SONGMENE V,AU Y J. The use of acoustic emission information to distinguish between dry and lubricated rolling element bearings in low-speed rotating machines[J]. The International Journal of Advanced Manufacturing Technology,2013,69(9-12):2679-2689.
[25] CAESARENDRA W,KOSASIH B,TIEU A K,et al. Acoustic emission-based condition monitoring methods:Review and application for low speed slew bearing[J]. Mechanical Systems and Signal Processing,2016,72:134-159.
[26] HAN Long,LI Chengwei,GUO Songlin,et al. Feature extraction method of bearing AE signal based on improved FAST-ICA and wavelet packet energy[J]. Mechanical Systems and Signal Processing,2015,62:91-99.
[27] 李潇,杨国安,吴贞焕,等. 基于声发射信号模糊函数综合相关系数的海洋平台各类损伤识别方法[J]. 机械工程学报,2014,50(22):1-9. LI Xiao,YANG Guoan,WU Zhenhuan,et al. Damage identification method of offshore platforms by ambiguity function integrated correlation coefficient of the acoustic emission signals[J]. Journal of Mechanical Engineering,2014,50(22):1-9.
[28] TRAN V T,YANG B-S,GU F,BALL A. Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing,2013,38(2):601-614.
[29] KIM J,LE M,LEE J,et al. Eddy current testing and evaluation of far-side corrosion around rivet in jet-engine intake of aging supersonic aircraft[J]. Journal of Nondestructive Evaluation,2014,33(4):471-480.
[30] SIMM A,WANG Q,HUANG S,et al. Laser based measurement for the monitoring of shaft misalignment[J]. Measurement,2016,87:104-116.
[31] 刘若晨,左洪福,孙见忠,等. 车辆齿轮箱静电监测[J]. 交通运输工程学报,2015,15(1):50-57. LIU Ruochen,ZUO Hongfu,SUN Jianzhong,et al. Electrostatic monitoring of vehicle gearbox[J]. Journal of Traffic and Transportation Engineering,2015,15(1):50-57.
[32] 李宏坤,张晓雯,贺长波,等. 利用随机共振的叶片裂纹微弱信息增强方法[J]. 机械工程学报,2016,52(1):94-101. LI Hongkun,ZHANG Xiaowen,HE Changbo,et al. Weak fault enhancement method for blade crack by using stochastic resonance[J]. Journal of Mechanical Engineering,2016,52(1):94-101.
[33] 周祖德,谭跃刚,刘明尧,等. 机械系统光纤光栅分布动态监测与诊断的现状与发展[J]. 机械工程学报,2013,49(19):55-69. ZHOU Zude,TAN Yuegang,LIU Mingyao,et al. Actualities and development on dynamic monitoring and diagnosis with distributed fiber bragg grating in mechanical systems[J]. Journal of Mechanical Engineering,2013,49(19):55-69.
[34] PRIETO M D,CIRRINCIONE G,ESPINOSA A G,et al. Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks[J]. IEEE Transactions on Industrial Electronics,2013,60(8):3398-3407.
[35] MARK W D. Time-synchronous-averaging of gear-meshing-vibration transducer responses for elimination of harmonic contributions from the mating gear and the gear pair[J]. Mechanical Systems and Signal Processing,2015,62:21-29.
[36] MOURA E P D,JUNIOR F E D A M,DAMASCENO F F R,et al. Classification of imbalance levels in a scaled wind turbine through detrended fluctuation analysis of vibration signals[J]. Renewable Energy,2016,96:993-1002.
[37] CHU Fulei,LU Wenxiu. Experimental observation of nonlinear vibrations in a rub-impact rotor system[J]. Journal of Sound and Vibration,2005,283(3):621-643.
[38] 陈予恕. 机械故障诊断的非线性动力学原理[J]. 机械工程学报,2007,43(1):25-34. CHEN Yushu. Nonlinear dynamical principle of mechanical fault diagnosis[J]. Journal of Mechanical Engineering,2007,43(1):25-34.
[39] CHEN Zhaowen,GAO Ning,SUN Wei,et al. A signal based triangular structuring element for mathematical morphological analysis and its application in rolling element bearing fault diagnosis[J]. Shock and Vibration,2014,2014:1-16.
[40] YAN Ruqiang,LIU Yongbin,GAO R X. Permutation entropy:A nonlinear statistical measure for status characterization of rotary machines[J]. Mechanical Systems and Signal Processing,2012,29:474-484.
[41] BELLINI A,YAZIDI A,FILIPPETTI F,et al. High frequency resolution techniques for rotor fault detection of induction machines[J]. IEEE Transactions on Industrial Electronics,2008,55(12):4200-4209.
[42] GU F,SHAO Y,HU N,et al. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment[J]. Mechanical Systems and Signal Processing,2011,25(1):360-372.
[43] BORGHESANI P,PENNACCHI P,RANDALL R,et al. Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions[J]. Mechanical Systems and Signal Processing,2013,36(2):370-384.
[44] LAMRAOUI M,THOMAS M,El BADAOUI M. Cyclostationarity approach for monitoring chatter and tool wear in high speed milling[J]. Mechanical Systems and Signal Processing,2014,44(1):177-198.
[45] 屈梁生,史东锋. 全息谱十年:回顾与展望[J]. 振动测试与诊断,1998,18(4):235-242. QU Liangsheng,SHI Dongfeng. Holospectrum during the past decade review & prospect[J]. Journal of Vibration,Measurement & Diagnosis,1998,18(4):235-242.
[46] HE Guolin,DING Kang,LI Weihua,et al. A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique[J]. Renewable Energy,2016,87:364-375.
[47] FAN Wei,CAI Gaigai,ZHU Zhongkui,et al. Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction[J]. Mechanical Systems and Signal Processing,2015,56-57:230-245.
[48] LIANG B,IWNICKI S,BALL A,et al. Adaptive noise cancelling and time-frequency techniques for rail surface defect detection[J]. Mechanical Systems and Signal Processing,2015,54:41-51.
[49] CLIMENTE-ALARCON V,ANTONINO-DAVIU J,RIERA-GUASP M,et al. Application of the Wigner-Ville distribution for the detection of rotor asymmetries and eccentricity through high-order harmonics[J]. Electric Power Systems Research,2012,91:28-36.
[50] THAMEUR K,MARC T,RAYNALD G,et al. Comparison between the efficiency of LMD and EMD algorithms for early detection of gear defects[J]. Mechanics & Industry,2013,14(2):121-127.
[51] SHI J,LIANG M,NECSULESCU D-S,et al. Generalized stepwise demodulation transform and synchrosqueezing for time-frequency analysis and bearing fault diagnosis[J]. Journal of Sound and Vibration,2016,368:202-222.
[52] PENG Zhike,PETER W T,CHU Fulei. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J]. Mechanical Systems and Signal Processing,2005,19(5):974-988.
[53] 雷亚国. 基于改进Hilbert-Huang变换的机械故障诊断[J]. 机械工程学报,2011,47(5):71-77. LEI Yaguo. Machinery fault diagnosis based on improved Hilbert-Huang transform[J]. Journal of Mechanical Engineering,2011,47(5):71-77.
[54] 程军圣,郑近德,杨宇. 一种新的非平稳信号分析方法-局部特征尺度分解法[J]. 振动工程学报,2012,25(2):215-220. CHENG Junsheng,ZHENG Jinde,YANG Yu. A nonstationary signal analysis approach-the local characteristic-scale decomposition method[J]. Journal of Vibration Engineering,2012,25(2):215-220.
[55] FENG Zhipeng,CHEN Xiaowang,LIANG M. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions[J]. Mechanical Systems and Signal Processing,2015,52-53:360-375.
[56] TAMILSELVAN P,WANG P. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety,2013,115:124-135.
[57] SCHLECHTINGEN M,SANTOS I F. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection[J]. Mechanical Systems and Signal Processing,2011,25(5):1849-1875.
[58] LEI Y,ZUO M J. Gear crack level identification based on weighted K nearest neighbor classification algorithm[J]. Mechanical Systems and Signal Processing,2009,23(5):1535-1547.
[59] WEST G M,MCARTHUR S D J,TOWLE D. Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring[J]. Expert Systems with Applications,2012,39(39):7432-7440.
[60] AMAR M,GONDAL I,WILSON C. Vibration spectrum imaging:A novel bearing fault classification approach[J]. IEEE Transactions on Industrial Electronics,2015,62(1):494-502.
[61] AL-BUGHARBEE H,TRENDAFILOVA I. A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling[J]. Journal of Sound and Vibration,2016,369:246-265.
[62] SEERA M,LIM C P,NAHAVANDI S,et al. Condition monitoring of induction motors:A review and an application of an ensemble of hybrid intelligent models[J]. Expert Systems with Applications,2014,41(10):4891-4903.
[63] 高金吉. 装备系统故障自愈原理研究[J]. 中国工程科学,2005,7(5):43-48. GAO Jinji. Research on the fault self-recovery principle of equipment system[J]. Engineering Science,2005,7(5):43-48.
[64] 张琳,孙安全,王天一,等. 某型导弹装备的故障智能诊断[J]. 中南大学学报,2013(S1):216-220. ZHANG Lin,SUN Anquan,WANG Tianyi,et al. Intelligent fault diagnosis of certain missile equipment[J]. Journal of Central South University,2013(S1):216-220.
[65] SHEN Changqing,WANG Dong,KONG Fanrang,et al. Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier[J]. Measurement,2013,46(4):1551-1564.
[66] JIANG Huiming,CHEN Jin,DONG Guangming,et al. Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis[J]. Mechanical Systems and Signal Processing,2015,52:338-359.
[67] 李巍华,翁胜龙,张绍辉. 一种萤火虫神经网络及在轴承故障诊断中的应用[J]. 机械工程学报,2015,51(7):99-106. LI Weihua,WENG Shenglong,ZHANG Shaohui. A firefly neural network and its application in bearing fault diagnosis[J]. Journal of Mechanical Engineering,2015,51(7):99-106.
[68] ZENG Ming,YANG Yu,ZHENG Jinde,et al. Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing,2016,66:533-545.
[69] GEBRAEEL N,LAWLEY M,LIU R,et al. Residual life predictions from vibration-based degradation signals:a neural network approach[J]. IEEE Transactions on Industrial Electronics,2004,51(3):694-700.
[70] ZIO E,DI M F. A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system[J]. Reliability Engineering & System Safety,2010,95(1):49-57.
[71] CHEN C,VACHTSEVANOS G,ORCHARD M E. Machine remaining useful life prediction:An integrated adaptive neuro-fuzzy and high-order particle filtering approach[J]. Mechanical Systems and Signal Processing,2012,28:597-607.
[72] TIAN Z. An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring[J]. Journal of Intelligent Manufacturing,2009,23(2):143-148.
[73] LOUTAS T H,ROULIAS D,GEORGOULAS G. Remaining useful life estimation in rolling bearings utilizing data-driven probabilistic e-support vectors regression[J]. IEEE Transactions on Reliability,2013,62(4):821-832.
[74] SIKORSKA J,HODKIEWICZ M,MA L. Prognostic modelling options for remaining useful life estimation by industry[J]. Mechanical Systems and Signal Processing,2011,25(5):1803-1836.
[75] LEE J,WU F,ZHAO W,et al. Prognostics and health management design for rotary machinery systems-Reviews,methodology and applications[J]. Mechanical Systems and Signal Processing,2014,42(1):314-334.
[76] WANG X,BALAKRISHNAN N,GUO B,et al. Residual life estimation based on bivariate non-stationary gamma degradation process[J]. Journal of Statistical Computation and Simulation,2015,85(2):405-421.
[77] PENG Weiwen,LI Yanfeng,YANG Yuanjian,et al. Inverse Gaussian process models for degradation analysis:A Bayesian perspective[J]. Reliability Engineering & System Safety,2014,130:175-189.
[78] 何兆民,王少萍. 基于时变状态转移隐半马尔科夫模型的寿命预测[J]. 湖南大学学报,2014,41(8):47-53. HE Zhaomin,WANG Shaoping. Remaining lifetime prediction based on time-varying state transition probabilities of hidden semi-markov model[J]. Journal of Hunan University,2014,41(8):47-53.
[79] 张星辉,康建设,赵劲松,等. 基于混合高斯输出贝叶斯信念网络模型的设备退化状态识别与剩余使用寿命预测方法研究[J]. 振动与冲击,2014,33(8):171-179. ZHANG Xinghui,KANG Jianshe,ZHAO Jinsong,et al. Equipment degradation state identification and residual life prediction based on MoG-BBN[J]. Journal of Vibration and Shock,2014,33(8):171-179.
[80] TSE P W,SHEN C. Remaining useful life estimation of slurry pumps using the health status probability estimation provided by support vector machine[M]. Springer International Publishing,2015.
[81] 雷亚国,陈吴,李乃鹏,等. 自适应多核组合相关向量机预测方法及其在机械设备剩余寿命预测中的应用[J]. 机械工程学报,2016,52(1):87-93. LEI Yaguo,CHEN Wu,LI Naipeng,et al. A relevance vector machine prediction method based on adaptive multi-kernel combination and its application to remaining useful life prediction of machinery[J]. Journal of Mechanical Engineering,2016,52(1):87-93.
[82] 周东华,魏慕恒,司小胜. 工业过程异常检测,寿命预测与维修决策的研究进展[J]. 自动化学报,2013,39(6):711-722. ZHOU Donghua,WEI Muheng,SI Xiaosheng. A survey on anomaly detection,life prediction and maintenance decision for industrial processes[J]. Acta Automatica Sinica,2013,39(6):711-722.
Options
Outlines

/

〈 〉

　 Copyright © Journal of Mechanical Engineering, All Rights Reserved.
Powered by Beijing Magtech Co. Ltd,.