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

A self-encoder and power equipment technology, applied in the direction of instruments, measuring electronics, measuring devices, etc., can solve problems such as low work efficiency, high equipment requirements, loss, etc., and achieve the effect of improving accuracy

Active Publication Date: 2021-02-19
杭州拓深科技有限公司
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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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  • Power equipment fault monitoring method based on mutual reconstruction single-class auto-encoder
  • Power equipment fault monitoring method based on mutual reconstruction single-class auto-encoder
  • Power equipment fault monitoring method based on mutual reconstruction single-class auto-encoder

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Embodiment Construction

[0046] The present invention will be described in further detail below in conjunction with the examples, but the protection scope of the present invention is not limited thereto.

[0047] The invention relates to a power equipment fault monitoring method based on a mutual reconstruction single-class self-encoder. like figure 1 As shown, the method of the present invention includes the following steps.

[0048]Step 1: Collect the magnetic field information of the power equipment in normal operation as the training data sample set.

[0049] Step 2: Perform preprocessing such as frame division, windowing, and noise reduction on the collected data samples, and obtain a training data sample set of N data samples [X] D×N =[x 1 … x N ], where D is the dimensionality of each data sample. N is the number of training data samples.

[0050] Step 3: Assume that a total of 2 mutual reconstruction single-class random autoencoders WSI-GAE are used, and the training data sample set X i...

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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 ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N20/00G06Q50/06G01R31/00
CPCG06N20/00G06Q50/06G01R31/00G06N3/048G06F18/24Y04S10/52
Inventor 张轩铭曹九稳王天磊梁昆王利强
Owner 杭州拓深科技有限公司
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