Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Probabilistic Wavelet Neural Network

Abstract

A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today's fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on industrial drives leads to higher current flow rate and the broken bearing detected system determined the number of unhealthy bearings but need to develop a faster system with constant frequency domain. Vibration data acquisition was used in our proposed work to detect broken bearing faults in induction machine. To generate an effective fault detection of industrial drives, Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN) system was proposed in this paper. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate and execution time.

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

“…The proposed ANN methodology could detect and locate ITSC fault, while the fuzzy approach was capable of detecting and diagnosing the severity of ITSC fault. A study of real time industrial drive vibration signal data for broken bearing detection using probabilistic wavelet NN was presented by Jayakumar and Thangavel (2015) in order to increase the fault detection rate and to handle larger power demand. An ANN-based FD/D approach for stator winding turn faults was presented by based on analysis of the (RTHF-FFT) magnitude component of the three-phase stator line current.…”

Motor fault detection using sound signature and wavelet transform

“…Meanwhile, in DWT, convolutions with a quadratic mirror filter are performed for the decomposition process of the original signal. As a result, a predetermined filters bank of low and high-passes used to transfer raw data of the original signal into orthonormal wavelet basis or decomposing the signal by a set of independent frequency bands to remove half of the frequency spectrum at each decomposition levels without risking the signal information components [24]− [26]. DWT would have the advantage of processing and analyzing stationary signals and non-stationary signals over the FFT [27].…”