Intelligent Sensors for Fault Diagnosis in Power Equipment
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 25 August 2026 | Viewed by 6
Special Issue Editors
Interests: application of new optical fiber sensing technology in state detection of electric power equipment; intelligent sensing system and instrument design
Special Issue Information
Dear Colleagues,
Power equipment, including transformers, circuit breakers, motors, and generators, forms the backbone of modern electrical grids. Unexpected failures of such assets can lead to severe economic losses, long outage durations, and safety hazards. Early detection, accurate fault diagnosis, and predictive maintenance have therefore become critical to ensure reliability, stability, and efficiency in power systems.
Recent advancements in intelligent sensing technologies—integrating high-precision data acquisition with AI-driven analytics—offer new opportunities for real-time monitoring and diagnosis of electrical equipment. Multi-modal sensor systems, including temperature, vibration, acoustic, partial discharge, and electrical parameter sensors, along with modern IoT-based architectures, enable continuous condition monitoring and fault prediction. Novel algorithms exploiting signal processing, machine learning, and deep learning can analyze complex data streams, support root cause analysis, and optimize asset management strategies.
This Special Issue on "Intelligent Sensors for Fault Diagnosis in Power Equipment" invites contributions presenting innovative sensor designs, integrated monitoring systems, and intelligent diagnostic methods in real-world power system applications. Topics include hardware, algorithms, communication protocols, and case studies from laboratories and industry.
Topics of interest include, but are not limited to, the following:
- Intelligent sensor design for electrical machinery and equipment;
- Multi-parameter sensing (temperature, vibration, acoustic, partial discharge, etc.);
- IoT-enabled monitoring platforms for power systems;
- Machine learning and deep learning for fault diagnosis;
- Data fusion and advanced signal processing in sensor systems;
- Sensor-based predictive maintenance for transformers, motors, switchgear;
- Real-time monitoring and protection systems in smart grids;
- Edge computing and embedded AI for condition monitoring;
- Wireless sensor networks for distributed asset management;
- Cybersecurity in sensor-based monitoring systems.
Prof. Dr. Hui Ding
Guest Editor
Dr. Shudong Wang
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- intelligent sensor design for electrical machinery and equipment
- multi-parameter sensing (temperature, vibration, acoustic, partial discharge, etc.)
- IoT-enabled monitoring platforms for power systems
- machine learning and deep learning for fault diagnosis
- data fusion and advanced signal processing in sensor systems
- sensor-based predictive maintenance for transformers, motors, switchgear
- real-time monitoring and protection systems in smart grids
- edge computing and embedded ai for condition monitoring
- wireless sensor networks for distributed asset management
- cybersecurity in sensor-based monitoring systems
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