New Advancements of AI for Fault Detection and Identification Systems for Industrial Automation or Asset Management
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 June 2025 | Viewed by 94
Special Issue Editors
Interests: artificial intelligence; machine learning; signal processing; embedded systems (FPGA/SoC); fault detection; identification systems; AI in education (AIEd); learning analytics; higher education policy
Special Issues, Collections and Topics in MDPI journals
Interests: AI (artificial intelligence); cognitive computing; enhancing security using block chain technology; semantic analytics; big data analytics; enhancing the learning curve of students in the era of bots
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; cyber security; health informatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, there has been a significant technological shift from narrow to generative AI (GenAI), driven by large language models. This massive change and the generic technological nature of AI make it suitable for any application ranging from healthcare, agriculture, business, fashion and art, education, and the supply chain to the retail market.
In this Special Issue, we invite high-quality contributions that explore the latest advancements in the design, development, and application of AI algorithms, architectures, and intelligent systems. Specifically, we are interested in submissions related to cutting-edge innovations in artificial intelligence, machine learning, deep learning, and GenAI and their integration into real-world applications. The main areas of focus include but are not limited to the following:
- AI-based fault detection and identification algorithms.
- Machine learning for predictive maintenance in motors.
- AI-driven motor condition monitoring systems.
- Industrial asset management by using AI.
- AI-based predictive and condition monitoring systems.
- AI-based anomaly detection.
- Real-time data analysis and decision making using AI in fault diagnosis.
- Integration of AI with IoT devices for smart monitoring and supply chain optimization.
We welcome original research, case studies, and reviews that highlight practical applications, theoretical advancements, and future trends in AI. Through this collection, we aim to provide valuable insights into the ongoing transformation of industries by AI and its growing impact on society. Moreover, we seek papers that highlight technical innovations and address ethical challenges, sustainability, and the human–AI collaboration necessary for robust deployment.
Dr. Tayab Din Memon
Dr. Nandini Sidnal
Dr. Kamran Shaukat
Guest Editors
Manuscript Submission Information
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Keywords
- fault detection and diagnosis (FDD)
- artificial intelligence (AI) in industrial automation
- predictive maintenance
- motor condition monitoring
- machine learning for fault detection
- deep learning in fault diagnosis
- smart sensors and IoT in industrial systems
- edge AI in industrial automation
- real-time fault detection
- intelligent monitoring systems
- anomaly detection in motors
- condition-based monitoring (CBM)
- AI-driven industrial control systems
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