Abstract
The requisite of direct-on-line (DOL) starting for various applications in underground mines subjects the rotor bars of heavy-duty squirrel cage induction motors (SCIMs) to severe stresses, resulting in sustained fault in the rotor bars, unlike the applications where mostly reduced voltage starting is preferred. Furthermore, SCIMs working in underground mines are also affected by unforeseen frequency fluctuations. Hence, the paper proposes a discrete wavelet transform (DWT)-based broken rotor bar detection scheme using the stator current analysis of SCIM when subjected to a frequency regulation (±4% of 50 Hz supply) in steady-state, as prevalent in underground mines. In this regard, the level-seven detailed coefficient obtained by the DWT-based multi-resolution analysis of stator current corresponding to the healthy rotor is compared with that of the faulty rotor to extract the necessary features to identify the fault. Further implementation of the proposed scheme is done using artificial neural network (ANN)-based pattern recognition techniques, wherein both feed-forward backdrops and cascaded forward backdrop type ANNs have been used for fault pinpointing based on the feature extraction results obtained from DWT. The scheme is developed and analysed in MATLAB/Simulink using 5.5 kW, 415 V, 50 Hz SCIM, which is further validated using the LabVIEW-based real-time implementation.
Highlights
Pervasive applications, low cost, reasonably small size, ruggedness, and low maintenance requirement have made induction motor (IM) the mainstay of industrial prime movers
The present section deals with the fast Fourier transform (FFT), discrete wavelet transform (DWT), and artificial neural network (ANN) results obtained using the designed simulation model
MCSA using FFT is done on the phase-a current signal in MATLAB/Simulink in the following sub-sections [36]
Summary
Low cost, reasonably small size, ruggedness, and low maintenance requirement have made induction motor (IM) the mainstay of industrial prime movers. The impact of axial cooling ducts, analysis of texture characteristics, feature extraction, and pattern classification using DWT-based techniques for the rotor fault detection in IM are discussed in [23,24,25] These WT-based approaches discussed so far have not reported their applicability for non-stationary signals in a steady state. Vibration signals are used for feature extraction, followed by a hybrid feature reduction technique It is evident from the recent research that while some of the works employ FFT for feature extraction, which makes it non-applicable in a transient state, the rest of the work uses the hybrid method on ANN, which is likely to make the computational process highly cumbersome. This proposed approach’s execution requires a minimum instrumentation system compared to the schemes and algorithms used in the available and presented literature [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25], which is highly desirable for the scheme’s reliable working under dusty and hazardous mine environments
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