Probabilistic Frequency-Domain Discrete Wavelet Transform for Better Detection of Bearing Faults in Induction Motors ?
Due to the importance of induction motors continuous operation, early detection of faults has become a major trend. As reported in an Institute of Electrical and Electronics Engineers study, bearing failures account for more than half of mechanical faults. To detect the existence of bearing failures, methods such as (short-time) Fourier, (continuous-discrete) wavelet, and Park transforms have already been introduced in similar research works. Static modeling of fault behavior was determined to be the major deficiency in the above-mentioned methods. In other words, using conventional detection techniques, a fault is assumed to have deterministic behavior, in which the fault frequencies are constant. In fact, fault characteristics can be affected depending on load or environmental conditions, which invalidate the conventional view. We developed our previously introduced technique, frequency-domain discrete wavelet transform (FD-DWT) into a stochastic model. This makes the detection process valid for a greater variety of fault conditions, and leads to earlier detection of faults and less damage to motors, compared to other strategies.
frequency-domain discrete wavelet transform stochastic modeling fault diagnosis inner/outer race bearing faults monte-carlo modeling