Three-phase asynchronous motor fault diagnosis device and method

Abstract

The invention discloses a three-phase asynchronous motor fault diagnosis device and method. The three-phase asynchronous motor fault diagnosis device comprises a terminal sensing system used for sensing various electrical parameters of a three-phase asynchronous motor; a remote data center used for constructing a three-phase asynchronous motor digital twinborn body and a fault diagnosis model. A three-phase asynchronous motor fault diagnosis model is constructed through deep learning, and is trained through a large amount of abundant data sets generated by digital twin bodies, so that the detection accuracy of the fault diagnosis model is greatly improved; and through transfer learning, the fault diagnosis model can be updated only through a small number of new samples, and the robustness and stability of fault diagnosis model detection of the three-phase asynchronous motor are improved.

Description

technical field [0001] The invention relates to the field of a three-phase asynchronous motor fault diagnosis device and method, in particular to a three-phase asynchronous motor fault diagnosis device and method. Background technique [0002] As the main driving force and driving device in industrial production, the three-phase asynchronous motor is its core equipment in coal mines, energy, chemical industry and other industrial fields. Once the three-phase asynchronous motor fails, the entire industrial operation system may face the risk of complete paralysis , causing unpredictable economic losses, and even endangering the lives of on-site staff. Therefore, the fault detection and fault diagnosis of three-phase asynchronous motor has important research significance. [0003] At present, most of the fault diagnosis of three-phase asynchronous motors is in the stage of on-site detection and diagnosis, and a small part has begun to use remote virtual simulation diagnosis. ...

AI Technical Summary

Problems solved by technology

[0002] As the main driving force and driving device in industrial production, the three-phase asynchronous motor is its core equipment in coal mines, energy, chemical industry and other industrial fields. Once the three-phase asynchronous motor fails, the entire industrial operation system may face the risk of complete paralysis , causing unpredictable economic losses, and even endangering the lives of on-site staff

On-site diagnosis requires experts to go to the work site for measurement and diagnosis. Limited by space, work efficiency is low

Remote virtual simulation, limited by sensors and communication technology, cannot be mapped in real time, so the accuracy of three-phase asynchronous motor fault diagnosis is low, and the real-time performance is poor

With the continuous development of big data and artificial intelligence technology, the fault diagnosis methods of three-phase asynchronous motors are also continuously improved. The existing data-driven fault diagnosis methods for three-phase asynchronous motors need to collect a large amount of actual data before they can be used. Training, but the three-phase asynchronous motor will inevitably be affected by its own wear and working environment changes during use. The fault diagnosis model trained before is no longer applicable. Retraining requires collecting a large number of three-phase motors in various states. Asynchronous motor operation data, time-consuming and low efficiency

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