Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

Tomasz Ciszewski,Len Gelman,Andrew Ball

doi:10.3390/electronics9101560

Tomasz Ciszewski, Len Gelman + Show 1 more

Open Access

https://doi.org/10.3390/electronics9101560

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Abstract

It is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology effectively detects component faults. The higher order spectral triple correlation technology successfully applied for rolling bearing fault identification. Experimental investigation of the technology has shown, that the technology effectively identifies rolling bearing fault by electrical data processing at very early stage of fault development. Novel technology comparisons via modelling and experiments of the proposed higher order spectral triple correlation technology and the higher order spectra technology show the higher fault identification effectiveness of the proposed technology over the bicoherence technology.

Highlights

Induction motors are used in a broad scope of industrial applications: electric vehicles, gearmotors, etc
The proposed method can significantly reduce the negative influence of the supply frequency component spectrum leakage, and can enhance the fault features to detect bearing outer raceway fault under lower load conditions
Further novel contribution from theoretical development of the proposed technologies is made for non-stationary operation of electromechanical systems via applying various time-frequency transforms for the higher order spectral multiple cross-correlations

Summary

Introduction

Induction motors are used in a broad scope of industrial applications: electric vehicles, gearmotors, etc. It is important to keep induction motors operational via fault identification, i.e., fault detection and isolation (FDI) of their key components. One of the key components that essentially impacts induction motor functions is its rolling bearings. Fault identification and prognosis for bearings are the subjects of many research works that provide viable results [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. A new technique for early fault detection and diagnosis in rolling-element bearings, based on vibration analysis is presented [1]. After normalization and the wavelet transform of vibration signals, the standard deviation as a measure of average energy and the logarithmic energy entropy as a measure of the degree of disorder are extracted in sub-bands of interest as representative features

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