Energy imaging system and application thereof in motor device fault detection method

Abstract

The invention discloses an energy imaging system and an application thereof in a motor device fault detection method. The system comprises a plurality of collection device terminals, a plurality of edge computing devices, and a cloud server. Each edge computing device is in communication connection with at least one collection device terminal. The cloud server is in communication connection with each edge computing device. The motor device fault detection method adopts the system and a device fault classification algorithm based on a deep learning improved sparse autoencoder algorithm. The system and the method can accurately predict potential faults that may occur in the operation of the motor device, so as to achieve the gain effect of early prevention and overall repair. Meanwhile, a traditional cloud computing method with large computational amount, complicated computing process, and high requirements on hardware equipment is replaced by an edge computing method with small computational amount, fast computational speed, and low requirements on hardware, so that the computational efficiency is improved.

Description

technical field [0001] The invention relates to the field of fault detection methods, in particular to a fault detection method for motor equipment. Background technique [0002] As the core driving device of electrical equipment, induction motor plays a vital role in modern industry. Especially in factories and enterprises such as large-scale pipeline production, the drive motors in industrial machinery are crucial to production automation. Therefore, it is particularly necessary and critical for the smooth progress of production work and the healthy development of the enterprise to maintain the normal operating state of the drive motor. Rotor eccentricity and rotor broken shaft are two main common faults of induction motors. Rotating rotors generate centrifugal force and eccentricity that is detrimental to equipment life. The rotor bars with limited service life are prone to breakage due to overload and improper maintenance. Once these failures occur, the entire produc...

AI Technical Summary

Problems solved by technology

Diagnostic data cannot be fully exploited when inappropriate signatures are used

At the same time, the versatility and accuracy of training classification models are also limited, especially for different types of electrical equipment operating in dynamic operating environments.

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