Trustworthy and Intelligent Systems for Machine Health Monitoring and Predictive Maintenance

Dear Colleagues,

In modern industrial systems, the continuous monitoring of machinery health is crucial for implementing predictive maintenance strategies. Real-time condition monitoring not only facilitates the early detection of potential faults but also helps avoid unnecessary maintenance activities, thereby sustaining optimal system performance.

Within condition-based maintenance, both diagnosis and prognosis are integral and complementary processes. Diagnosis involves assessing the historical and current health status of machinery using monitored signal data. At the same time, prognosis focuses on predicting the remaining useful life based on past and ongoing operational profiles.

Various methodologies have been developed for diagnosing and prognosing machinery health. Among these, data-driven approaches leveraging machine learning and deep learning techniques have gained significant attention in recent years. These methods are often considered more adaptable and scalable alternatives to physics-based models, which may struggle to incorporate real-time updates of health data. Despite the promise of data-driven approaches in uncovering correlations between operational data and equipment health, reliably detecting incipient faults and forecasting future machine conditions in a trustworthy and interpretable manner remains a significant challenge. Thus, these areas continue to represent key research challenges in machinery health management.

This Special Issue invites contributions from both academic researchers and industry practitioners. It seeks to showcase the latest theoretical advances and practical applications in trustworthy data-driven health monitoring for intelligent machinery. We welcome the submission of experimental studies as well as theoretical papers, with the expectation that the latter offer novel insights and feasible solutions to relevant industrial problems. Potential topics include, but are not limited to:

• Dynamics modelling and simulation of machines;
• Data cleaning and data quality improvement;
• Condition monitoring and health assessment;
• Signal processing and fault feature extraction;
• Fault detection and quantitative analysis;
• Data-driven intelligent fault diagnosis and prognosis;
• Vibration analysis of components of machines;
• Big model for general prognostics and health management.

Dr. Tongtong Yan
Dr. Yaoxiang Yu
Dr. Yankai Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• dynamics modelling and simulation
• machine health monitoring
• fault diagnosis in deep learning
• performance degradation assessment
• big model