Bearing fault analysis by signal energy calculation based signal processing technique in Squirrel Cage Induction Motor

Squirrel Cage Induction Motor (SCIM) is largely prevalent machine which are in employment for conversion of electrical energy into mechanical energy in an assorted nature of applications. Bearings are most symptomatic components of mechanical faults occurring in SCIM as they are held accountable for 40-50 % of all the motor malfunctions. Fast Fourier Transform (FFT) is an imperative spectrum estimation technique, comprehensively used with Motor Current Signature Analysis (MCSA). However FFT does not endow with substantial outcome when frequency resolution is considered. Multi Resolution Analysis (MRA) can be used to analyze any signal for obtaining better resolution. Discrete Wavelet Transform (DWT) gives out better idea about the variation in specific frequency band all through the bearing fault(s). In this paper, for minimalism and fast analysis, signal energy is calculated. The stator current data is acquired by creating different fault cases on a 3-phase, 1.5kW, 4P, and 1440 RPM induction motor. The variation in the energy of healthy and energy of faulty condition(s) bestow with a better idea pertaining to fault detection and classification. Actual data analysis divulges that FFT is not suitable for practical circumstances. After analyzing different cases of bearing fault, it can evidently be concluded that DWT has an edge over FFT. Some attributes regarding bearing fault classification are also obtained.