Intelligent heat and vibration monitoring and shutdown prevention
By using machine learning models to monitor temperature and vibration in busbar trunking, the shortcomings of traditional temperature threshold monitoring are overcome, enabling rapid and accurate anomaly detection in busbar trunking, reducing downtime risks, and improving equipment safety and reliability.
CN120978664APending Publication Date: 2025-11-18SCHNEIDER ELECTRIC USA INC
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Patent Information
- Application Number
- CN202510534289.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2024-05-15
- Filing Date
- 2025-04-27
- Publication Date
- 2025-11-18
AI Technical Summary
Technical Problem
Existing technologies rely solely on temperature threshold monitoring in busbar trunking, making it difficult to quickly and accurately detect overheating and mechanical faults, leading to potential operational failures and safety hazards.
Method used
By employing machine learning models combined with temperature and vibration sensors, the system monitors the normal thermal and vibration conditions of the busbar trunking. By modeling environmental conditions and loads to predict anomalies, it generates electronic or visual notifications.
Benefits of technology
It enables rapid and accurate anomaly detection of busbar trunking, reducing downtime and improving equipment safety and reliability.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN120978664A_ABST
Abstract
Intelligent monitoring of heat and vibration of electrical distribution equipment. A plurality of sensors provide temperature and vibration data related to a bus or busway of the electrical distribution device. A diagnostic processor processes sensor data initially received from the sensors and load data representative of an electrical load of the electrical distribution device as inputs to a trained machine learning model to predict a response to the electrical load. A diagnostic processor processes sensor data and load data subsequently received from the sensor during operation of the electrical load to determine whether the sensor data subsequently received from the sensor significantly deviates from a predicted response to the electrical load, and generates an electronic or visual notification based on the determination.
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