JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 123-129.doi: 10.6040/j.issn.1672-3961.0.2017.166

Previous Articles     Next Articles

A mode-correlation principal component analysis for the fault detection of marine current turbine

ZHANG Milu, WANG Tianzhen*, TANG Tianhao, XIN Bin   

  1. Department of Electrical Automation, Shanghai Maritime University, Shanghai 201306, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

PDF (PC)

625

Abstract: To solve the problem of multi-mode characteristic and frequent mode changes, a detection method for marine current turbine which called mode-correlation principal component analysis was proposed. The influence of modal change on the traditional principal component analysis(PCA)was analyzed in theory. The detection problem caused by multi-mode characteristic was described. A mode normalized algorithm was proposed in the proposed method to dynamic fitting the mode. The statistical difference value of different modes was removed due to relationships between modes. Compared with other methods, the experimental platform was built to verify the effectiveness of the proposed method. Theoretical analysis and experimental results showed that the proposed method could detect the fault quickly and accurately under the condition of variable speed and variable load.

Key words: mode-correlation PCA, dynamic time warping, PCA regression, fault detection

CLC Number: 

  • TP273
[1] BENELGHALI S, BENBOUZID M E H, CHARPENTIER J F. Generator systems for marine current turbine applications: a comparative study[J]. IEEE Journal of Oceanic Engineering, 2012, 37(3):554-563.
[2] ZHANG M, WANG T, TANG T, et al. An imbalance fault detection method based on data normalization and EMD for marine current turbines[J]. Isa Transactions, 2017, 68:302-312.
[3] 温广瑞, 江铖, 李杨,等. 基于弗德卡曼阶比跟踪的转子起停车故障特征提取方法及应用[J]. 振动与冲击, 2016, 35(2):64-68. WEN Guangrui, JIANG Cheng, LI Yang, et al. Fault feature extraction from the vibration signals in rotor start-up or slowdown processes based on order tracking and holospectrum[J]. Journal of Vibration and Shock, 2016, 35(2):64-68.
[4] WANG J, GAO R X, YAN R. Multi-scale enveloping order spectrogram for rotating machine health diagnosis[J]. Mechanical Systems and Signal Processing, 2014, 46(1):28-44.
[5] ZIMROZ R, BARTELMUS W, BARSZCZ T, et al. Diagnostics of bearings in presence of strong operating conditions non-stationarity: a procedure of load-dependent features processing with application to wind turbine bearings[J]. Mechanical Systems and Signal Processing, 2014, 46(1):16-27.
[6] ZHAO F, TIAN Z, BECBHHOEFER E, et al. An integrated prognostics method under time-varying operating conditions[J]. IEEE Transactions on Reliability, 2015, 64(2):1-14.
[7] GONG X, QIAO W. Current-based mechanical fault detection for direct-drive wind turbines via synchronous sampling and impulse detection[J]. IEEE Transactions on Industrial Electronics, 2015, 62(3):1693-1702.
[8] ZHANG M, WANG T, TANG T, et al. Imbalance fault detection of marine current turbine under condition of wave and turbulence[C] //Conference of the IEEE Industrial Electronics Society, 2016. Florence, Italy: IEEE, 2016:6353-6358.
[9] 王天真, 吴昊, 刘萍,等. 基于动态限的周期非稳定工况的实时故障检测模型[J]. 电工技术学报, 2014, 29(12):95-101. WANG Tianzhen, WU Hao, LIU Ping, et al. Real-time fault detection model based on dynamic limit under periodic non-steady condition[J]. Transactions of China Electro technical Society, 2014, 29(12):95-101.
[10] YU J. A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis[J]. Journal of Process Control, 2012, 22(4):778-788.
[11] MA Y, SHI H, MA H, et al. Dynamic process monitoring using adaptive local outlier factor[J]. Chemometrics and Intelligent Laboratory Systems, 2013, 127(18):89-101.
[12] 王天真, 高迪驹, 刘萍,等. 基于动态限的非周期非稳定工况故障检测模型[J]. 上海交通大学学报, 2012, 46(4):607-612. WANG Tianzhen, GAO Diju, LIU Ping, et al. A fault detection model based on dynamic limit under non-periodic non-steady conditions[J]. Journal of Shanghai Jiaotong University, 2012, 46(4):607-612.
[13] WANG T, WU H, NI M, et al. An adaptive confidence limit for periodic non-steady conditions fault detection[J]. Mechanical Systems and Signal Processing, 2015, 72-73:328-345.
[14] WANG G, LIU J, ZHANG Y, et al. A novel multi-mode data processing method and its application in industrial process monitoring[J]. Journal of Chemometrics, 2014, 29(2):126-138.
[15] QIN S J. Statistical process monitoring: basics and beyond[J]. Journal of Chemometrics, 2003, 17(8-9):480-502.
[16] WANG T, XU H, HAN J, et al. Cascaded H-bridge multilevel inverter system fault diagnosis using a PCA and multiclass relevance vector machine approach[J]. IEEE Transactions on Power Electronics, 2015, 30(12):7006-7018.
[17] LICHTENAUER J F, HENDRIKS E A, REINDERS M J T. Sign language recognition by combining statistical DTW and independent classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11):2040-2046.
[18] 齐咏生, 王普, 高学金,等. 基于多阶段动态PCA的发酵过程故障监测[J]. 北京工业大学学报, 2012,38(10):1474-1481. QI Yongsheng, WANG Pu, GAO Xuejin, et al. Fault detection for fermentation process based on multiphase dynamic PCA[J]. Journal of Beijing University of Technology, 2012, 38(10):1474-1481.
[19] 于静, 田国会, 尹建芹. 基于深度信息的动态手势识别算法[J]. 山东大学学报(工学版), 2014, 44(3):52-56. YU Jing, TIAN Guohui, YIN Jianqin. Dynamic hand gesture recognition algorithm based on depth information[J]. Journal of Shandong University(Engineering Science), 2014, 44(3):52-56.
[20] LUST E, FLACK K, LUZNIK L. The influence of depth and surface waves on marine current turbine performance[J]. International Journal of Marine Energy, 2013(3):27-40.
[1] LIU Yang. Effects of multiplicative actuator faults on the fault diagnosis performance in open-loop and closed-loop systems [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 38-43.
[2] YANG Rui. Intermittent fault detection method based on sparse representation [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 51-56.
[3] LI Hongyang, HE Xiao. A fault detection and estimation scheme for nonlinear stochastic systems based on SCKF [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 130-135.
[4] CHEN Zhiwen, PENG Tao, YANG Chunhua , HE Zhangming, YANG Chao, YANG Xiaoyue. A fault detection method based on modified canonical correlation analysis [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 44-50.
[5] CHEN Jie, ZHONG Maiying, ZHANG Ligang. Fault detection of unmanned aerial vehicle flight control system based on optimal estimation of the L2-norm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 89-95.
[6] ZHAO Xuan, ZHONG Maiying, GUO Dingfei. Parity space-based fault detection for unmanned aerial vehicle flight control systems [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 150-156.
[7] WANG Huiqing, SUN Hongwei, ZHANG Jianhui. Time series similarity searching algorithm based on Map/Reduce [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(1): 15-21.
[8] YU Jing1, TIAN Guohui1*, YIN Jianqin2. Dynamic hand gesture recognition algorithm based on depth information [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(3): 52-56.
[9] JI Tao,SUN Tong-jing,XU Bing-yin,SUN Bo . Integrated methods to detect earth fault in DC systems [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 55-59 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd