Energy-Aware and Trustworthy Fault Diagnosis and Prognostics for Industrial Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 10 October 2026 | Viewed by 106
Special Issue Editors
Interests: fault diagnosis; soft sensor; industry applicatinos; intelligent fault diagnosis
Special Issue Information
Dear Colleagues,
Digital technologies offer significant opportunities for companies to enhance manufacturing efficiency, reliability, and operational performance. Continuous monitoring of industrial systems is essential to prevent unexpected production interruptions caused by faults or equipment degradation. In this context, intelligent fault diagnosis and condition monitoring systems play a crucial role by enabling early detection, identification, and prognosis of anomalies during machine operation. These systems integrate advanced sensing, signal processing, and artificial intelligence techniques to support predictive maintenance strategies. By identifying failures before breakdowns occur, such approaches reduce downtime, improve system safety, and increase productivity, contributing to the advancement of smart manufacturing and Industry 4.0 environments.
Recommended topics include, but are not limited to, the following:
- Energy‑aware and sustainability‑oriented fault diagnosis and prognostics.
- Industrial experiments, large‑scale case studies, and Technology Readiness Level (TRL)‑oriented validation.
- Fault detection, diagnosis, and prognosis methods for real‑world production systems.
- Explainable, interpretable, and trustworthy diagnostic and prognostic models, including uncertainty quantification.
- Innovative sensing principles, soft sensors, and advanced feature engineering for industrial monitoring.
- AI technologies such as machine learning, computer vision, natural language processing, and expert systems applied to real‑time industrial diagnostics.
- Sustainable and energy‑efficient maintenance strategies enabled by intelligent monitoring systems.
Dr. André Luís Dias
Dr. Maira Martins Da Silva
Guest Editors
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Keywords
- fault detection
- fault diagnosis
- fault prognosis
- condition monitoring
- health monitoring
- feature engineering
- AI-based methods
- industry applications
- industrial systems
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