Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networks

TL;DR

This paper introduces a novel thermal image-based fault diagnosis method for induction machines using Self-Organized Operational Neural Networks, which achieve comparable accuracy to deep CNNs but with simpler, edge-deployable architecture.

Contribution

The study presents the application of Self-Organized Operational Neural Networks for fault diagnosis in induction motors, demonstrating their effectiveness and efficiency over traditional CNNs.

Findings

Self-ONNs achieve similar diagnostic accuracy as complex CNNs.

Self-ONNs have a shallower architecture with three operational layers.

The approach is suitable for deployment on edge devices.

Abstract

Condition monitoring of induction machines is crucial to prevent costly interruptions and equipment failure. Mechanical faults such as misalignment and rotor issues are among the most common problems encountered in industrial environments. To effectively monitor and detect these faults, a variety of sensors, including accelerometers, current sensors, temperature sensors, and microphones, are employed in the field. As a non-contact alternative, thermal imaging offers a powerful monitoring solution by capturing temperature variations in machines with thermal cameras. In this study, we propose using 2-dimensional Self-Organized Operational Neural Networks (Self-ONNs) to diagnose misalignment and broken rotor faults from thermal images of squirrel-cage induction motors. We evaluate our approach by benchmarking its performance against widely used Convolutional Neural Networks (CNNs), including ResNet, EfficientNet, PP-LCNet, SEMNASNet, and MixNet, using a Workswell InfraRed Camera (WIC). Our results demonstrate that Self-ONNs, with their non-linear neurons and self-organizing capability, achieve diagnostic performance comparable to more complex CNN models while utilizing a shallower architecture with just three operational layers. Its streamlined architecture ensures high performance and is well-suited for deployment on edge devices, enabling its use also in more complex multi-function and/or multi-device monitoring systems.