Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

Abstract

Stator inter-turn fault diagnosis system for electric motors is of a considerable concern due to its significant effect on industrial production. In this paper, a new method for detecting the inter-turn fault and quantifying its severity in the line start permanent magnet synchronous motor (LSMPSM) is proposed. The new method depends on monitoring the stator current during steady-state period to detect the fault. The convolutional neural network (CNN) method is proposed to correlate the motor steady-state current with the status of the motor winding conditions and detect any presence of inter-turn faults. The data used in this study is extracted from both an experimental setup of a one-horsepower LSPMSM and the corresponding verified mathematical model through several testing cases under various loading conditions. One of the main features of the proposed technique is that it does not require separate feature extraction phase. The results indicate that the proposed technique is able to detect the inter-turn fault under different loading conditions varies from 0NM to 4NM with accuracy of 97.75% for all defined fault levels. The use of steady-state current for fault detection regardless of motor load enables the proposed technique to detect the fault online without disturbing the system functionality and reliability as well as without adding any extra hardware to the system. INDEX TERMS Convolutional neural network (CNN), diagnosis, fault detection, inter-turn fault, LSPMSM.

Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems

“…Liu et al [18] used a 2D-CNN based model for fault diagnosis of motor bearings and centrifugal pumps; the approach extracts fault features from images constructed by continuous wavelet transform. Maraaba et al [19] developed a fault diagnosis method for permanent magnet synchronous motors based on 2D-CNN. In this paper, 2D matrices, which are composed of three-phase steady-state motor currents, are adopted as the input data.…”

“…Among many DL techniques, the convolutional neural network (CNN) is widely used in many fields, thanks to its advantages, which include local connectivity, parameter sharing, and the ability to consider high-dimensional information within the input data [14]- [16]. Due to the merits of the CNN approach, it has also been actively utilized to develop intelligent machine fault diagnosis methods [17]- [19].…”

Recognition of Stator Winding Inter-Turn Fault in Interior-Mount LSPMSM Using Acoustic Signals

“…The instantaneous speed was used to investigate the effects of the faults on the transient behavior of the motor. In [16][17][18], a neural-network-based diagnostic tool for detecting inter-turn faults in an LSPMSM was developed that uses three cycles of the steady-state stator current for detecting the fault. The authors in [16,18] detected the severity of fault, while in [17], the phase location and severity of the fault were detected by using the three-phase currents.…”

“…In [16][17][18], a neural-network-based diagnostic tool for detecting inter-turn faults in an LSPMSM was developed that uses three cycles of the steady-state stator current for detecting the fault. The authors in [16,18] detected the severity of fault, while in [17], the phase location and severity of the fault were detected by using the three-phase currents. It worth mentioning that, in [17], a JMAG™-based finite element mathematical model was developed and utilized to analyze the fault as well as for the generation of training and testing data, whereas in [18], both simulation and experimental data were used.…”

“…The authors in [16,18] detected the severity of fault, while in [17], the phase location and severity of the fault were detected by using the three-phase currents. It worth mentioning that, in [17], a JMAG™-based finite element mathematical model was developed and utilized to analyze the fault as well as for the generation of training and testing data, whereas in [18], both simulation and experimental data were used. In addition, in [16,17], time and frequency domain analysis was carried out to extract the fault representative features from the current, while in [18], the three-phase instantaneous steady state current was used as an input to the convolutional neural network.…”