Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems

Submission Deadline: 30 June 2025 (closed) View: 1387 Submit to Special Issue

Guest Editors

Dr. Jia Uddin, Woosong University, South Korea
Dr. Md Junayed Hasan, Robert Gordon University, UK

Summary

This special issue delves into the latest advancements in machine fault diagnosis and prognosis, with a focus on industrial equipment and autonomous systems. Emphasizing the critical role of data-driven approaches, the issue explores innovative solutions in machine fault diagnosis, prognosis of industrial equipment, and the implementation of autonomous systems. The integration of multimodal sensing and explainable AI enhances the reliability and efficiency of predictive maintenance and real-time fault detection. The incorporation of cloud-based automation and industrial IoT is pivotal in advancing condition monitoring and smart manufacturing processes.

By leveraging machine learning, deep learning, and big data analytics, researchers are developing sophisticated diagnostic algorithms that contribute to the robustness of cyber-physical systems and overall industrial automation. This collection of papers aims to provide a comprehensive overview of cutting-edge research and practical applications in the field, offering valuable insights into reliability engineering, sensor fusion, and the broader spectrum of industrial applications.

Keywords

Machine Fault Diagnosis, Prognosis of Industrial Equipment, Autonomous Systems, Data-Driven Approaches, Multimodal Sensing, Explainable AI, Cloud-Based Automation, Predictive Maintenance, Industrial IoT, Machine Learning, Deep Learning, Condition Monitoring, Smart Manufacturing, Reliability Engineering, Real-Time Fault Detection, Sensor Fusion, Big Data Analytics, Cyber-Physical Systems, Industrial Automation, Diagnostic Algorithms

Published Papers

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ARTICLE

A Deep Learning Approach for Fault Diagnosis in Centrifugal Pumps through Wavelet Coherent Analysis and S-Transform Scalograms with CNN-KAN
Muhammad Farooq Siddique, Saif Ullah, Jong-Myon Kim
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3577-3603, 2025, DOI:10.32604/cmc.2025.065326
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract Centrifugal Pumps (CPs) are critical machine components in many industries, and their efficient operation and reliable Fault Diagnosis (FD) are essential for minimizing downtime and maintenance costs. This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps. The proposed method combines Wavelet Coherent Analysis (WCA) and Stockwell Transform (S-transform) scalograms with Sobel and non-local means filters, effectively capturing complex fault signatures from vibration signals. Using Convolutional Neural Network (CNN) for feature extraction, the method transforms these scalograms into image inputs, enabling the recognition of More >

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ARTICLE

Rolling Bearing Fault Diagnosis Based on Cross-Attention Fusion WDCNN and BILSTM
Yingyong Zou, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4699-4723, 2025, DOI:10.32604/cmc.2025.062625
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >

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ARTICLE

Steel Surface Defect Detection Using Learnable Memory Vision Transformer
Syed Tasnimul Karim Ayon, Farhan Md. Siraj, Jia Uddin
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >

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Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems

Guest Editors

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Published Papers

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266

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364

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926

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