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
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