Data Science in Prognostics and Health Management of Industrial Equipment

Dear Colleagues,

Currently, data science plays a crucial role in the field of Prognostics and Health Management (PHM) of industrial equipment, revolutionizing the way we monitor, diagnose, predict, and maintain equipment health. By leveraging advanced data analytics techniques, data science enables us to extract valuable insights from vast amounts of data generated by equipment sensors and monitoring systems. However, in various industrial scenarios, there still exist many unsolved problems or challenges within the PHM field, for instance, zero-shot or few-shot diagnosis tasks, interpretability models, noisy signal processing, usage of simulation data, etc. Researchers are actively exploring innovative approaches to tackle these persistent issues and enhance the effectiveness of PHM practices. Many journal papers or conference papers aiming at these problems have been published. But, for different equipment objects, it is difficult to determine a general, mature, and feasible solution. That implies that the field of PHM requires a wide range of diverse research to adapt to various scenarios. Therefore, this Special Issue welcomes all research dedicated to solving problems related to the field of PHM. We believe that your research can contribute valuable insights to PHM, whether in algorithms/methods, frameworks, or applications.

The scopes of this Special Issue included but are not limited to the following:

1. Big Data Analytics for Industrial PHM;
2. Internet of Things (IoT) and Sensor Data Fusion in PHM;
3. Large Language Models (LLMs) for Industrial PHM;
4. Explainable Artificial Intelligence in PHM;
5. Digital Twins for Industrial Equipment Maintenance;
6. Reliability or Trustworthy PHM in Practical Industry;
7. Uncertainty Quantification for Data-Driven PHM Methods;
8. Federated Learning or Edge Computing for Real-Time PHM;
9. Advanced Signal Processing Methods for Industrial PHM;
10. Real-World Case Studies and Applications of PHM.

Dr. Ruyi Huang
Dr. Xi Zhang
Dr. Jianguo Miao
Guest Editors

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