JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 30-37.doi: 10.6040/j.issn.1672-3961.0.2017.193

Previous Articles     Next Articles

Early diagnosis and life prognosis for slowlyvarying fault based on deep learning

ZHOU Funa1, GAO Yulin1*, WANG Jiayu1, WEN Chenglin2   

  1. 1. School of Computer and Information Engineering, Henan University, Kaifeng 475004, Henan, China;
    2. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

Abstract: In order to overcome the shortcoming of traditional early fault diagnosis methods, a method of combining deep learning with PCA to realize early diagnosis of slowly varying small fault and life prognosis was proposed. Using the deep learning method to extract the sampled data characteristics layer by layer, learning the early fault characteristics and establishing the early fault diagnosis model for slowly varying small fault, the combining deep learning was combined with PCA to integrate the high dimensional fault feature vector extracted by the deep learning into a fault characteristic variable. A data-driven fault precursor could be defined according to the evolution rule of the characteristic variable of the historical fault data, and life prognosis model was established by exponential nonlinear fitting method. The TE benchmark data was used to verify the effectiveness of the proposed algorithm, experimental results showed the validity of the proposed algorithm by comparing with other algorithms.

Key words: early diagnosis, life prognosis, slowly varying faults, nonlinear fitting, deep learning

CLC Number: 

  • TP277
[1] SHANG Chao, YANG Fan, HUANG Dexian, et al. Data-driven soft sensor development based on deep learning technique[J]. Journal of Process Control, 2014, 24(3):223-233.
[2] ZHOU Funa, PARK Ju H, LIU Yajuan. Differential feature based hierarchical PCA fault detection method for dynamic fault[J]. Neurocomputing, 2016, 202:27-35.
[3] 庞荣, 余志斌, 熊维毅,等. 基于深度学习的高速列车转向架故障识别[J]. 铁道科学与工程学报, 2015,12(6):1283-1288. PANG Rong, YU Zhibin, XIONG Weiyi, et al. Faults recognition of high-speed train bogie based on deep learning[J]. Journal of Railway Science and Engineering, 2015, 12(6):1283-1288.
[4] CHEN Xiaoyue, ZHOU Jianzhong, XIAO Jian, et al. Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings[J].Applied Mathematics and Computation, 2014, 247:835-847.
[5] 周东华, 胡艳艳. 动态系统的故障诊断技术[J].自动化学报, 2009,35(6):748-758. ZHOU Donghua, HU Yanyan. Fault diagnosis techniques for dynamic systems[J]. Acta Automatica Sinica, 2009, 35(6):748-758.
[6] 李娟, 周东华, 司小胜,等. 微小故障诊断方法综述[J]. 控制理论与应用, 2012, 29(12):1517-1529. LI Juan, ZHOU Donghua, SI Xiaosheng, et al. Review of incipient fault diagnosis methods[J] ,Control Theory and Applications, 2012, 29(12):1517-1529.
[7] 葛志强, 杨春节, 宋执环. 基于MEWMA-PCA的微小故障检测方法研究及其应用[J]. 信息与控制, 2007, 36(5):650-656. GE Zhiqiang, YANG Chunjie, SONG Zhihuan. Research and application of small shifts detection method based on MEWMA-PCA[J]. Information and Control, 2007, 36(5):650-656.
[8] 董小亮, 帕孜来·马合木提. 混杂系统传感器微小故障的检测与隔离方法[J].自动化与仪表, 2015, 30(11):13-17. DONG Xiaoliang, PAZILAI Mahemuti. Sensor incipient fault detection and isolation method of hybrid system[J].Automation and Instrumentation, 2015, 30(11):13-17.
[9] 邱天, 白晓静, 郑茜予,等. 多元指数加权移动平均主元分析的微小故障检测[J].控制理论与应用, 2014, 31(1):19-26. QIU Tian, BAI Xiaojing, ZHENG Xiyu, et al. Incipient fault detection of multivariate exponentially weighted[J]. Control Theory and Applications, 2014, 31(1):19-26.
[10] 周福娜, 文成林, 陈志国,等. 基于指定元分析的多级相对微小故障诊断方法[J].电子学报, 2010, 38(8):1874-1879. ZHOU Funa, WEN Chenglin, CHEN Zhiguo, et al. DCA based multi-level small fault diagnosis method[J].Acta Electronica Sinica, 2010, 38(8):1874-1879.
[11] 孙美红, 孙巍, 赵劲松,等. 多变量统计方法监测化工过程的缓变故障[J].计算机与应用化学, 2009, 26(10):1228-1232. SUN Meihong, SUN Wei, ZHAO Jinsong, et al. MCUSUM-MSPCA based small shift monitoring in TE process[J].Computers and Applied Chemistry, 2009, 26(10):1228-1232.
[12] 文成林, 吕菲亚, 包哲静,等. 基于数据驱动的微小故障诊断方法综述[J].自动化学报, 2016, 42(9):1285-1299. WEN Chenglin, LYU Feiya, BAO Zhejing, et al. A review of data driven-based incipient fault diagnosis[J].Acta Automatica Sinica, 2016, 42(9):1285-1299.
[13] JIA Feng, LEI Yaguo, LIN Jing, et al. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J].Mechanical Systems and Signal Processing, 2016, 72-73:303-315.
[14] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[15] 孙志军, 薛磊, 许阳明,等.深度学习研究综述[J].计算机应用研究,2012,29(8):2806-2810. SUN Zhijun, XUE Lei, XU Yangming, et al. Overview of deep learning[J]. Application Research of Computers, 2012, 29(8):2806-2810.
[16] 彭宇, 刘大同. 数据驱动故障预测和健康管理综述[J].仪器仪表学报,2014, 35(3):481-495. PENG Yu, LIU Datong. Data-driven prognostics and health management: a review of recent advances[J]. Chinese Journal of Scientific Instrument, 2014, 35(3):481-495.
[17] LIAO Haitao, ZHAO Wenbiao, GUO Huairui. Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model[C] //Proceeding of Rams'06: Annual Reliability and Maintainability Symposium.Newport Beach, USA, CA:IEEE, 2006:127-132.
[18] 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.
[19] SIKORSKA J Z, 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.
[20] LIAO Linxia, K(¨overO)TTIG Felix. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction[J]. Reliability IEEE Transactions on, 2014, 63(1):191-207.
[21] LU Chen, WANG Zhenya, QIN Weili, et al. Fault diagnosis of rotary machinery components using a stacked denoisingautoencoder-based health state identification[J].Signal Processing, 2017, 130:377-388.
[1] XIE Zhifeng, WU Jiaping, MA Lizhuang. Chinese financial news classification method based on convolutional neural network [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 34-39.
[2] TANG Leshuang, TIAN Guohui, HUANG Bin. An object fusion recognition algorithm based on DSmT [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 50-56.
[3] HE Zhengyi, ZENG Xianhua, QU Shengwei, WU Zhilong. The time series prediction model based on integrated deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 40-47.
[4] ZHENG Yi, ZHU Chengzhang. A prediction method of atmospheric PM2.5 based on DBNs [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(6): 19-25.
Full text



No Suggested Reading articles found!