Intelligent Data Mining and Decision Making for Prognostics and Health Management
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 1560
Special Issue Editor
Special Issue Information
Dear Colleagues,
Data mining and decision making are the foundation of data-driven mathematical applications. Prognostics and Health Management (PHM) is a process that integrates data mining and decision making, including multiple stages such as fault detection, fault diagnosis, remaining useful life prediction, health status assessment, and condition-based maintenance. In PHM applications driven by big data, intelligent and ideal results of data mining and decision making will greatly help it achieve benefits.
In this Special Issue, we focus on the latest developments in data mining and decision making for health management applications, including but not limited to health management data governance, information fusion, feature extraction, fault knowledge mining, sensor layout optimization, fault detection, fault diagnosis, remaining useful life prediction, health status assessment, optimized maintenance, and some other works related to health management. We encourage the submission of these interdisciplinary achievements that are conducive to PHM technology research so they can be published in this Special Issue, such as data analysis and decision making in medicine, data mining in Internet big data applications, large language model applications, and other intelligent research.
Dr. Mingliang Suo
Guest Editor
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Keywords
- data mining
- decision making
- uncertainty measurement
- information fusion
- feature extraction
- fault knowledge mining
- sensor layout optimization
- fault-tolerant control
- fault reconstruction
- fault detection
- fault diagnosis
- remaining useful life prediction
- health status assessment
- optimized maintenance
- medical data analysis
- big data
- large language model
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