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Health Monitoring and Eccentricity Fault Diagnosis of Induction Motor by Signal Processing Techniques

Khadim Moin Siddiqui*, Bhavesh Kumar Chauhan**, Sonu Bala Garg***

*,** Babu Banarsi Das National Institute of Technology and Management (BBDNITM), Lucknow, Uttar Pradesh India.

*** IKG Punjab TechnicalUniversity, Jalandhar Campus, Punjab, India.

Periodicity:July - September'2019
DOI : https://doi.org/10.26634/jdp.7.3.17063

Abstract

In the present time, there is a strong need to develop an efficient health monitoring system to diagnose air-gap eccentricity fault of the induction motor at early stages. If the fault is diagnosed in the early stages then one can save the industry for millions of dollars. The main aim of the researchers is to develop a non-intrusive health monitoring system for induction motor health detection in relatively low cost and also ought to be powerful for detection of developing online faults in the early stages. In the induction motor, due to unbalanced magnetic pull, the airgap eccentricity faults occur and if this fault is not diagnosed in the early stages then it will lead to large revenue losses for the industry. This issue has been addressed in this research paper and an effort is made to give an competent health monitoring technique for this kind of fault detection purpose. To achieve better results, hybrid technique has been used to extract relevant information of the fault from the raw signal in the developing stage. The EMD and wavelet algorithm has been used jointly for efficient health monitoring purpose for inverter fed induction motor machine. Two techniques have been used for fault diagnosis purpose, one is FFT technique and the other is hybrid technique. It has been observed that the hybrid technique has given encouraging results over FFT technique.

Keywords

Eccentricity Fault Diagnosis, Empirical Decomposition, Hybrid Technique, Induction Motor, Motor Current Signature Analysis, Signal Processing.

How to Cite this Article?

Moinsiddiqui, K., Chauhan, B. K., and Garg, S. B. (2019). Health Monitoring and Eccentricity Fault Diagnosis of Induction Motor by Signal Processing Techniques. i-manager’s Journal on Digital Signal Processing. 7(3), 9-15. https://doi.org/10.26634/jdp.7.3.17063

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