An elevator hoisting machine fault diagnosis system and method based on multi-sensor fusion data
The elevator traction machine fault diagnosis system, which integrates multi-sensor data, solves the problems of data silos and small sample scenarios in elevator traction machine fault diagnosis, achieving efficient and reliable fault diagnosis and improving the diagnostic accuracy and adaptability of the model.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-05-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing elevator traction machine fault diagnosis technologies face the problem of data silos, high communication overhead and unreasonable resource allocation in federated learning, lack of physical consistency guarantee for data augmentation in small sample scenarios, and lack of organic coupling between digital twin simulation data and federated learning framework.
The elevator traction machine fault diagnosis system, which uses multi-sensor fusion data, forms a complete closed loop of simulation generation, federated training, and edge inference by combining cloud-based digital twin simulation data with fault-driven asynchronous federated communication, multi-modal semantic alignment multi-teacher knowledge distillation and aggregation, and a generative model that introduces physical consistency constraints.
It significantly reduces communication overhead, improves the diagnostic accuracy and reliability of the model under small sample conditions, ensures the physical rationality of the generated data, and adapts to the accuracy in non-independent and identically distributed scenarios.
Smart Images
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