Data-Driven Robust Fault Detection and Isolation of Three-Phase Induction Motor

This paper deals with data-driven fault diagnosis of three-phase induction motor. The tools from subspace identification based data-driven scheme are utilized to design a framework of fault detection and isolation. The fault detection scheme is so designed that it caters for even small magnitude faults. In order to discriminate different faults, a fault isolation algorithm is proposed. Isolation thresholds are designed. The results show the effectiveness of the proposed schemes.

[1]  S. Jagannathan,et al.  Model based diagnostics and prognostics of three-phase induction motor for vapor compressor applications , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[2]  Steven X. Ding,et al.  Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems , 2014 .

[3]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[4]  Chuanjiang Li,et al.  Subspace aided data-driven design of robust fault detection and isolation systems , 2011, Autom..

[5]  Shorouk Ossama Ibrahim,et al.  Implementation of fuzzy modeling system for faults detection and diagnosis in three phase induction motor drive system , 2015 .

[6]  Said Yahmedi,et al.  Robust Control Design of an Induction Motor , 2013 .

[7]  Sri Krishna.  Fault Detection in Three Phase Induction Motor , 2015 .

[8]  Steven X. Ding,et al.  A Review on Basic Data-Driven Approaches for Industrial Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.

[9]  Ping Zhang,et al.  Subspace method aided data-driven design of fault detection and isolation systems , 2009 .

[10]  Jong-Myon Kim,et al.  Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors , 2016, J. Sensors.

[11]  Elsa D. Angelini,et al.  An Unbiased Risk Estimator for Image Denoising in the Presence of Mixed Poisson–Gaussian Noise , 2014, IEEE Transactions on Image Processing.

[12]  Muhammad Abid,et al.  Design of Robust Fault Detection Scheme for Penicillin Fermentation Process , 2015 .

[13]  Steven X. Ding,et al.  Data-driven monitoring for stochastic systems and its application on batch process , 2013, Int. J. Syst. Sci..

[14]  Muhammad Abid,et al.  Robust Fault Detection Using Subspace Aided Data Driven Design , 2016 .

[15]  Steven D. Glaser,et al.  Wavelet denoising techniques with applications to experimental geophysical data , 2009, Signal Process..

[16]  Sauro Longhi,et al.  Induction motor fault detection and diagnosis using KDE and Kullback-Leibler divergence , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[17]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .