Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning

TL;DR

This paper introduces a novel fault diagnosis method for inter-turn short circuits in PMSMs using current signal imaging via recurrence plots and deep learning, enabling capacity-independent fault detection under various conditions.

Contribution

The study develops a robust, machine-independent feature extraction approach combining recurrence plots and CNNs for fault diagnosis in PMSMs, effective across different motor capacities.

Findings

Method accurately detects faults in different motor capacities.

Robust to environmental and operational variations.

Effective in noisy conditions.

Abstract

This paper proposes machine-independent feature engineering for winding inter-turn short circuit fault that uses electrical current signals. Electrical current signal collected from permanent magnet synchronous motor (PMSM) is subjected to different environmental and operational conditions. To solve these problems, robust current signal imaging method and deep learning-based feature extraction method are developed. The overall procedure includes the following three key steps: (1) transformation of a time-series current signal to two-dimensional image, (2) extracting features using convolutional neural networks, and (3) calculating a health indicator using Mahalanobis distance. Transformation of the time-series signal is based on recurrence plots (RP). The proposed RP method develops from feature engineering that provides the dominant fault feature representations in a robust way. The proposed RP is designed that maximizes the features of inter-turn short fault and minimizes the effect of noise from systems with various capacities. To demonstrate the validity of the proposed method, two case studies are conducted using an artificial fault seeded testbed with two different capacities of motor. By calculating the feature using only the electrical current signal of the motor without the parameters related to the capacity of the motor, the proposed feature can be applied to motors with different capacities while maintaining the same performance.