Development of Fault Diagnosis System for Teaching Equipment in Electrical Engineering Automation Major
Abstract
effectiveness and educational value, we developed a fault diagnosis system that collects equipment parameters through multi-sensor networks,
optimizes diagnostic algorithms using improved momentum-based BP neural networks, and implements multi-user interaction with remote
maintenance capabilities. The research demonstrates that this system effectively resolves issues of missed or misdiagnosed faults in conventional methods, generates fault case studies to support teaching, and provides technical support for equipment management and professional
skill development in university practical education.
Keywords
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DOI: http://dx.doi.org/10.70711/neet.v3i12.8247
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