Novel Investigation of Higher Order Spectral Technologies for Fault Diagnosis of Motor-Based Rotating Machinery

Abstract

In the last decade, research centered around the fault diagnosis of rotating machinery using non-contact techniques has been significantly on the rise. For the first time worldwide, innovative techniques for the diagnosis of rotating machinery, based on electrical motors, including generic, nonlinear, higher-order cross-correlations of spectral moduli of the third and fourth order (CCSM3 and CCSM4, respectively), have been comprehensively validated by modeling and experiments. The existing higher-order cross-correlations of complex spectra are not sufficiently effective for the fault diagnosis of rotating machinery. The novel technology CCSM3 was comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the validated technology, confirmed high overall probabilities of correct diagnosis for bearings at early stages of damage development. The novel diagnosis technologies were compared with existing diagnosis technologies, based on triple and fourth cross-correlations of the complex spectra. The comprehensive validation and comparison of the novel cross-correlation technologies confirmed an important non-traditional novel outcome: the technologies based on cross-correlations of spectral moduli were more effective for damage diagnosis than the technologies based on cross-correlations of the complex spectra. Experimental and simulation validations confirmed a high probability of correct diagnosis via the CCSM at the early stage of fault development. The average total probability of incorrect diagnosis for the CCSM3 for all experimental results of 8 tested bearings, estimated via 6528 diagnostic features, was 1.475%. The effectiveness gains in the total probability of incorrect diagnosis for the CCSM3 in comparison with the CCCS3 were 26.8 for the experimental validation and 18.9 for the simulation validation. The effectiveness gains in the Fisher criterion for the CCSM3 in comparison with the CCCS3 were 50.7 for the simulation validation and 104.7 for the experimental validation.

Induction Motor Stator Winding Inter-Tern Short Circuit Fault Detection Based on Start-Up Current Envelope Energy

“…However, this approach incurred substantial costs in terms of human labor and time. Currently, the main researched fault detection technologies include motor current signature analysis (MCSA) [4][5][6][7][8], vibration analysis [3,9,10], acoustic signal analysis [11][12][13], and thermal imaging analysis [14][15][16]. Jung et al [4] proposed an online diagnostic method for current signature analysis based on advanced signal-and data-processing algorithms, which successfully achieved the diagnosis of three types of faults, namely rotor misalignment, stator winding short circuit, and bearing defects, in induction motors.…”

“…Bouzida et al [6] proposed a fault diagnosis method based on the discrete wavelet transform, which extracts the health information of electric motors from wide-band signals utilizing wavelet decomposition. Ciszewski et al [7] proposed a current feature analysis diagnosis method based on higher-order spectral technology, which effectively identified bearing faults in induction motors. Kim et al [3] proposed a fault diagnosis method based on vibration signals, and utilized various machine learning models to achieve the classification of motor health, rotor faults, and bearing faults.…”