Deep Learning and Predictive Maintenance in Industrial Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 72
Special Issue Editors
Interests: deep and machine learning; knowledge graphs; semantic interoperability; transfer learning; explainable and transparent artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: industrial deep learning; machine learning; transfer learning; continual learning; lifelong learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The digital transformation of industry continues to unlock new potential for intelligent maintenance systems. This Special Issue focuses on how deep learning methods, such as convolutional neural networks, recurrent architectures, or transformer-based models, can drive progress in predictive maintenance by enabling robust representations of complex, high-dimensional industrial data.
We invite original contributions that explore deep learning approaches for key tasks in predictive maintenance, including early fault detection, remaining useful life estimation, anomaly detection, and the development of adaptive maintenance strategies. Particular emphasis is placed on real-world applications in manufacturing, energy systems, and critical infrastructure.
In addition to application-driven research, we welcome submissions that address theoretical and methodological advances in the context of predictive maintenance. This includes research on interpretable and robust deep models for time series and sensor data, data-efficient learning strategies such as transfer or few-shot learning, and approaches to multimodal sensor fusion or image-based diagnostics. Furthermore, contributions that reflect on the challenges of deploying such models in complex industrial environments are particularly encouraged.
This Special Issue will foster interdisciplinary dialogue between academia and industry, encouraging contributions that bridge methodological innovation and practical implementation. By bringing together diverse perspectives, we will advance the development of scalable, reliable, and intelligent maintenance solutions based on deep learning.
Prof. Dr. Tobias Meisen
Dr. Hasan Tercan
Guest Editors
Manuscript Submission Information
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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.
Keywords
- predictive maintenance
- industrial deep learning
- transformer models in industry
- remaining useful life estimation
- fault detection
- anomaly detection
- condition monitoring
- sensor fusion
- time series analysis
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