Work

Open Gate, Open Switch and Short Circuit Fault Detection of Three-Phase Inverter Switches in Induction Motor Drive Applications

Year: 2026

Type: article

Abstract: Electric motor drives with a wide variety of applications are usually derived with inverters, where the inverter switches are always prone to different types of faults. Short circuit faults can rapidly shut down systems, and open-circuit ones can lead to secondary damage if they are not detected and tolerated in time. Due to this fact, in this paper, a novel data-driven fault detection and diagnosis (FDD) method has been proposed to detect and locate all types of inverter switch faults. Three deep learning algorithms, including fully connected neural networks (FCNs), convolutional neural networks (CNNs), and bidirectional long short-term memory (BiLSTM), have been implemented and compared. The BiLSTM network with 98.45% accuracy outperforms the others and can detect all types of faults in less than half a fundamental period under different and variable speeds with the existence of noise. The results show that the proposed method is highly effective and is a great candidate for real-time applications.

Source: Actuators

DOI: 10.3390/act15010034

Authors: Mohammad Zamani Khaneghah, Mohamad Alzayed, Hicham Chaoui

Institution: Carleton University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896