Bearing Fault Diagnosis for an Induction Motor Controlled by an Artificial Neural Network—Direct Torque Control Using the Hilbert Transform

Abstract

Motor Current Signature Analysis (MCSA) is a popular method for the detection of faults in electric motor drives, particularly in Induction Machines (IMs). For Bearing Defects (BDs), which are very much related to the rotational frequency, it is important to maintain the speed at a target reference value in order to distinguish and locate the different BDs. This can be achieved by using a powerful control such as the Direct Torque Control (DTC), but this control causes the variation of the supply frequency and the current signal to become non-stationary, so the integration of advanced signal processing methods becomes necessary by using a suitable filter to handle the frequency content depending on the BDs, such as the Hilbert filter. This paper aims to adopt the Hilbert Transform (HT) for extracting the signature of the faults from the stator current envelope to detect the different BDs in the IMs when they are controlled by an intelligent DTC control driven by Artificial Neural Networks (ANN-DTC). This ANN-DTC control is a shaping factor rather than a disturbing one, which contributes with the Hilbert filter to the diagnosis of BDs. This technique is tested for the four locations of BDs: the inner ring, the outer ring, the ball, and the bearing cage in different operating situations without control and with conventional DTC and ANN-DTC controls. Thus, detecting the location of the defect exactly at an early stage contributes to achieving maintenance in a fairly short time. The performance of the chosen approach lies in minimizing the electromagnetic torque ripples as a result of the control and increase of the amplitudes of the spectra related to BDs compared to other harmonics. This performance is verified in the MATLAB/SIMULINK environment.

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“…The derivation of an analytic signal from a real-valued signal entails the utilization of the Hilbert transform (HT). The resultant analytic signal finds widespread application in signal processing and communication systems, serving diverse purposes such as analyzing frequency content, extracting envelope information, and facilitating phase-sensitive operations [47,48]. The HT of a real-valued signal f (t) is given by…”

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“…In literature, situations involving non-stationary signals or low loads necessitate the use of more potent signal processing tools because these conditions affect how responsive a system is for condition monitoring [18], [20]. On the plus side, HT is one of the signal processing tools that has proven to be extremely effective under these circumstances and has consequently grown in popularity in recent years [10], [18], [25].…”

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

“…equipment. According to the type of signals used, commonly used bearing fault diagnosis methods are classified as vibration analysis [4,5], current analysis [6][7][8], sound analysis [9], and other methods [10,11]. Bearing troubleshooting based on vibration signals is a widely used method because vibration signals can reflect the dynamic behavior and health of the equipment [12].…”