Improving Induction Motor fault classification accuracy through enhanced multimodal preprocessing, artificial image synthesis, deep learning and load-adaptive graph-based methods

Hejazi, Shahd 2024. Improving Induction Motor fault classification accuracy through enhanced multimodal preprocessing, artificial image synthesis, deep learning and load-adaptive graph-based methods. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

This thesis aims to improve the accuracy of fault classification in Induction Motor (IM) bearings by developing and applying advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques for condition monitoring data. The proposed framework utilises several approaches, namely, Multimodal Data Preprocessing, Artificial Thermal Image Creation, Customised Radial Load Assessment, Multimodal Systems Decision Fusion, and Graph Convolutional Networks (GCN) on Tabular Datasets to achieve better classification accuracies over existing methods. This study's first significant contribution is the proposed novel approach in the preprocessing of multimodal condition monitoring data for classifying induction motor faults that employs Convolutional Neural Networks (CNNs), such as Residual Network-18 (ResNet-18) and SqueezeNet, to fuse vibration signals and thermal images. This approach enhances fault classification accuracy by 14.81% and proves exceptionally effective in scenarios with compromised image quality. Further refinement using Gramian Angular Field (GAF) processing enhances the detection of subtle fault indicators, achieving better accuracy than Continuous Wavelet Transform (CWT). Secondly, this thesis explores the creation of high-quality artificial thermal images using Wasserstein GAN with Gradient Penalty (WGAN-GP) and its conditional variant, conditional Wasserstein GAN with Gradient Penalty (cWGAN-GP), to address the scarcity of thermal imaging data. The artificial thermal images replicate complex thermal patterns of IMs under various fault conditions with remarkable accuracy, as evidenced by the improved Maximum Mean Discrepancy (MMD) scores and a 40.00% reduction in training times. The high fidelity of these artificially generated images, validated against real images, underscores their practical use in fault classification. Thirdly, the Customised Load Adaptive Framework (CLAF) introduces a novel approach to incorporating load variations into fault classification. Through a two-phase process involving ANOVA and optimal CWT, load-dependent fault subclasses—Mild, Moderate, Severe, and Normal (fault-free) or Healthy—are identified. The CLAF achieved an accuracy of 96.30% ± 0.50% in 18.155 s during five-fold cross-validation using a Wide ii Neural Network (WNN), demonstrating its ability to detect subtle fault variations across different Load Factors (LFs). Fourthly, building upon the CLAF’s load-dependent fault subclass structure, the research proposed two key methodologies for enhancing load-specific condition monitoring accuracy while optimising training time relative to complexity using the MFPT bearing dataset namely, the Load-Dependent Multimodal Vibration Signal Enhancement and Fusion (LD-MVSEF) method, and the Hybrid Graph-CNN Decision Fusion (HG-CDF) method. The LD-MVSEF employs a multimodal approach across multiple channels, with different signal encoding techniques achieving a fault classification accuracy of 99.04% ± 0.22% over five runs in 18 min 30 s. It performed particularly well in the Moderate class, achieving 99.15% ± 0.89% testing accuracy, and scored 97.20% ± 1.75% in the Mild class. The proposed HG-CDF combines the structural strengths of Graph Convolutional Networks (GCNs) with the pattern-detection capabilities of 1D-Convolutional Neural Networks (1D-CNNs) for CLAF load-dependent fault subclass classification. The study began by optimising the GCN through Taguchi experiments, converting tabular data into graph structures using the k-Nearest Neighbours method and achieving a mean accuracy of 89.01% ± 1.25 across nine configurations. HG-CDF further improved performance, reaching an overall accuracy of 99.19% in just 3 minutes and 28 seconds, surpassing LD-MVSEF in the Mild class with 98.92% accuracy while also providing a faster and more efficient solution. The methodologies proposed in this research significantly enhance the IM fault classification task, improve the decision-making process, and offer scalable solutions adaptable to other domains.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Engineering
Uncontrolled Keywords:	1). Induction Motor 2). Fault Classification 3). Multimodal Preprocessing 4). Artificial Image Synthesis 5). Load-Adaptive Graph-Based Methods 6). Deep Learning
Date of First Compliant Deposit:	8 May 2025
Last Modified:	08 May 2025 08:19
URI:	https://orca.cardiff.ac.uk/id/eprint/178133

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