Optimization & Fault discovery of Induction Motor by Current Signature Analysis technique

Induction Motor (IM) faults, such as air gap eccentricity, rotor faults, short-circuits, and bearing faults, can be detected through conditional monitoring, which requires expensive tools. Digital Signal Processing (DSP) offers a cost-effective solution by using IM Current Signature Analysis (IMCSA) to identify faults. Faulty motors exhibit different frequency spectra in their line current compared to healthy ones due to harmonic components. IMCSA detects and analyzes these changes using Lab VIEW software for direct online monitoring. This paper discusses the impact of motor faults, fault tracking, and the use of Fast Fourier Transforms (FFT) for frequency spectrum analysis from analog current data, outlining the transformation process and experimental procedures for fault detection.