Intelligent Fault Diagnosis and Predictive Process Monitoring

Dear Colleagues,

As industrial systems evolve toward higher levels of complexity, automation, and connectivity, ensuring their operational reliability and safety becomes increasingly critical. Fault diagnosis and predictive monitoring emerge as key components in this scenario, supporting timely decision-making and minimizing downtime through early detection of anomalies, root cause identification, and failure forecasting. These capabilities are particularly relevant in high-stakes domains such as energy systems, manufacturing, transportation, and critical infrastructure, where faults can lead to cascading operational, economic, and environmental consequences.

Recent advances in artificial intelligence, signal processing, and hybrid modeling have enabled the development of intelligent fault diagnosis and prognostic systems capable of learning from historical and real-time data. Methods such as machine learning, deep learning, Bayesian reasoning, and physics-informed models are now being combined to create more robust, interpretable, and adaptive monitoring solutions. Nonetheless, significant challenges remain, including data scarcity, noisy sensor environments, model transferability across domains, and the integration of predictive outputs into maintenance and control routines.

This Special Issue welcomes original research articles, comprehensive reviews, and case-based studies that advance the design, development, and evaluation of intelligent techniques for fault diagnosis and predictive monitoring. Relevant contributions may address (but are not limited to) health indicator construction, degradation modeling, remaining useful life estimation, fault detection and classification, anomaly detection, and the integration of digital twin technologies. We are also interested in adaptive and semi-supervised learning strategies, transfer learning approaches, and hybrid models that combine data-driven methods with physical or expert knowledge. Submissions may explore algorithmic innovations, scalable architectures, explainability frameworks, benchmark datasets, or deployment strategies in real-world environments. Comparative studies, cross-domain validations, and interdisciplinary methodologies that bridge theoretical foundations with industrial applicability are particularly encouraged.

Dr. Miguel Angelo De Carvalho Michalski
Dr. Gilberto Francisco Martha de Souza
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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

• fault diagnosis
• predictive monitoring
• remaining useful life (RUL)
• health indicator construction
• anomaly detection
• condition-based maintenance
• digital twins
• hybrid modeling
• machine learning
• industrial prognostics