Fault detection and isolation based on fuzzy‐integral fusion approach

In this study, the bearing, as well as the electric faults of an induction motor, are diagnosed using the fuzzy-integral data-fusion method in feature level with high reliability. Time domain of various features is computed using the induction motor three-phase current and voltage measurements. Appropriate features are extracted by means of the proposed method and then classified by the fuzzy C -means algorithm. The fuzzy membership functions show the relation between a feature set and a fault to establish the mappings between the features and the given faults. Finally, different features are fused using the fuzzy-integral method to produce diagnostic results. The technique is validated experimentally on an induction motor coupled with a centrifugal pump. The capability of the proposed technique is also evaluated in the presence of disturbances and simultaneous occurrence of different faults. The results indicate an increase in the reliability in fault detection and isolation.

[1]  G. Klir,et al.  Fuzzy Measure Theory , 1993 .

[2]  Daqi Zhu,et al.  Information fusion fault diagnosis method for unmanned underwater vehicle thrusters , 2013 .

[3]  Wahyu Caesarendra,et al.  A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing , 2017 .

[4]  Javad Poshtan,et al.  Stator winding short-circuit fault diagnosis based on multi-sensor fuzzy data fusion , 2016 .

[5]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[6]  Hassan Ghasemzadeh,et al.  Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges , 2017, Inf. Fusion.

[7]  Yan Liang,et al.  Fault detection for multi-rate sensor fusion under multiple uncertainties , 2015 .

[8]  Zhiwei Gao,et al.  From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis , 2013, IEEE Transactions on Industrial Informatics.

[9]  Mu Xiaodong,et al.  Fault Diagnosis of Parts of Electronic Embedded System Based on Fuzzy Fusion Approach , 2009, 2009 First International Workshop on Education Technology and Computer Science.

[10]  Dilip Kumar Pratihar,et al.  A Comparative Study of Fuzzy C-Means Algorithm and Entropy-Based Fuzzy Clustering Algorithms , 2011, Comput. Informatics.

[11]  Peng Shi,et al.  Incipient fault diagnosis for T–S fuzzy systems with application to high-speed railway traction devices , 2016 .

[12]  Sylvain Verron,et al.  Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges , 2016, Annu. Rev. Control..

[13]  Gui Yun Tian,et al.  Data fusion algorithm for pulsed eddy current detection , 2007 .

[14]  Jorge A. Balazs,et al.  Opinion Mining and Information Fusion: A survey , 2016, Inf. Fusion.

[15]  Magdi S. Mahmoud,et al.  Enhanced distributed estimation based on prior information , 2015, IET Signal Process..

[16]  Xiaochen Zhang,et al.  Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition , 2017, Sensors.

[17]  Yuanqing Xia,et al.  Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures , 2016, IET Signal Process..

[18]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[19]  Jong-Myon Kim,et al.  A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis , 2017, Sensors.

[20]  Mainak Sengupta,et al.  Experimental study of induction motor misalignment and its online detection through data fusion , 2013 .

[21]  Shaocheng Wang,et al.  Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors , 2014, IEEE/ASME Transactions on Mechatronics.

[22]  Teng Li,et al.  Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder , 2017 .

[23]  Xiaofeng Liu,et al.  Machinery fault diagnostics based on fuzzy measure and fuzzy integral data fusion techniques , 2009 .