Power equipment fault monitoring method based on mutual reconstruction single-class auto-encoder

Abstract

The invention discloses a power equipment fault monitoring method based on a mutual reconstruction single-class auto-encoder.The method comprises the following steps: preprocessing acquired magnetic field information of power equipment in normal operation to obtain a training data sample set, training K mutual reconstruction single-class random auto-encoders WSI-GAE by taking the training data sample set as input to obtain a final encoding result, performing single classification model training by using regularized least square single classification loss, obtaining a fitting error of each trained data sample, and selecting a threshold from fitting error sequences of the data samples arranged from large to small, for newly collected magnetic field information data of the power equipment, the obtained fitting error is compared with a threshold value, and when the fitting error is larger than the threshold value, it can be judged that the power equipment has abnormal conditions such as faults. According to the invention, a single-class classifier technology is used to realize anomaly detection, and the method is more suitable for the target of the invention. The fault abnormity monitoring accuracy of the power equipment is improved.

Description

technical field [0001] The invention relates to the technical fields of electromagnetic induction and fault detection, in particular to a fault monitoring method for power equipment based on a mutual reconstruction single-class self-encoder. Background technique [0002] It is one of the most commonly used methods at present to judge whether the electrical equipment is faulty or abnormal by monitoring the magnetic field generated by the electrical equipment during operation. In the prior art, there are mainly two ways to detect the power equipment: 1) analysis and judgment by relatively large analyzers or detection equipment; 2) judgment based on the experience of technicians. However, the first method has higher requirements for equipment, and it will consume a lot of manpower and material resources only for disassembly and assembly. The second method requires higher professional experience, and there is an obvious shortage of personnel, and the work efficiency is low. If ...

AI Technical Summary

Problems solved by technology

However, the first method has high requirements for equipment, and it will consume a lot of manpower and material resources only on disassembly and assembly.

The second method requires higher professional experience, and there is an obvious shortage of personnel, and the work efficiency is low. If a problem occurs, it cannot be checked immediately, resulting in a certain degree of loss.

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