Next-Generation Machine Learning Models for Predictive Analytics in Condition Monitoring
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 6
Special Issue Editors
Interests: industrial large models; digital twins; embodied artificial intelligence; transfer learning; graph neural networks; information fusion; intelligent fault diagnosis and prognosis
Special Issues, Collections and Topics in MDPI journals
Interests: mechanical system condition monitoring and fault diagnosis; intelligent manufacturing
Interests: advanced signal processing methods and mechanical fault feature extraction technology; intelligent diagnostic methods and life prediction technology; performance testing technology for automotive components
Special Issue Information
Dear Colleagues,
Condition monitoring is essential for modern industrial intelligent maintenance, enhancing the reliability of equipment through predictive data analytics. Next-generation machine learning techniques overcome the limitations of traditional deep learning, such as data dependence and interpretability, and are better suited for industrial environments with large-scale data and high safety demands. Next-generation machine learning models include, but are not limited to, graph neural networks, causal inference, mixture-of-experts architectures, federated learning, small and large language models, physics-informed machine learning, spiking neural networks, and hybrid data-model driven frameworks. By integrating modern signal processing with advanced learning algorithms, these approaches excel not only in traditional monitoring tasks but also in open, dynamic, and complex industrial scenarios, enabling advanced functions such as uncertainty quantification, cross-modal reasoning, and real-time decision-making.
This Special Issue highlights cutting-edge research and applications of next-generation machine learning in predictive analytics for condition monitoring across sectors, including aerospace, rail transportation, energy and chemical engineering, agricultural machinery, and semiconductor manufacturing, with a focus on health monitoring and management of advanced manufacturing systems. We welcome submissions on novel methods, industrial cases, and cyber–physical system integrations.
Dr. Zihao Lei
Prof. Dr. Xiangfeng Zhang
Dr. Kun Zhang
Dr. Kai Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- condition monitoring
- industrial maintenance
- predictive analytics
- next-generation machine learning
- uncertainty analysis
- fault diagnosis
- remaining useful life (RUL) prediction
- real-time decision-making
- dynamics
- signal processing
- digital twins
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