Equipment fault detection classifier training method, computing equipment and storage medium

A training method and equipment failure technology, applied in computing, computer components, neural learning methods, etc., can solve problems such as omissions and inability of classifiers to obtain diagnostic results

Pending Publication Date: 2021-11-05
ANHUI RONDS SCI & TECH INC CO
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, after the feature set is removed, some features will be missed, which will cause the obtained classifier to fail to achieve good diagnostic results.

Method used

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  • Equipment fault detection classifier training method, computing equipment and storage medium
  • Equipment fault detection classifier training method, computing equipment and storage medium
  • Equipment fault detection classifier training method, computing equipment and storage medium

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Embodiment Construction

[0042] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. Like reference numbers generally refer to like parts or elements.

[0043] figure 1 A schematic diagram showing a communication connection between a server and a device according to an exemplary embodiment of the present invention. like figure 1 As shown, the server 140 is in communicative connection with the devices 110-130. The devices 110-130 may be implemented as a mechanical device, and the present invention does not limit th...

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Abstract

The invention discloses an equipment fault detection classifier training method, which comprises the following steps: acquiring an equipment operation data set from equipment, processing each operation data record of the data set to generate an original sample, and obtaining an original sample set comprising a plurality of original samples; encoding each original sample in the original sample set to generate an individual, and obtaining an initial population comprising a plurality of individuals; constructing a fitness function according to fault types included in the original sample set; performing genetic iteration on the population according to the fitness function to obtain a target population; determining an optimal individual in the target population; determining an optimized feature set of the original sample set according to the chromosome of the optimal individual; processing each original sample in the original sample set according to the optimized feature set to generate a training sample, and obtaining a training sample set comprising a plurality of training samples; and training the classifier according to the training sample set. The invention also discloses computing equipment and a computer readable storage medium.

Description

technical field [0001] The invention relates to the field of equipment fault diagnosis, in particular to a training method for equipment fault detection classifiers, computing equipment and storage media. Background technique [0002] With the development of computer and sensor technology, more and more computers are monitoring the operation of equipment and automatically judging the operation of equipment. Usually, by deploying sensors in the equipment, the operating data of the equipment is collected, and the operating data is analyzed by a computer to obtain the analysis results. However, due to the large number of operating data collection items and the complex operating conditions of the equipment, the corresponding relationship between operating data and equipment failure types is not clear enough. Therefore, a large number of original features have to be proposed for fault identification, but due to the constraints of many factors such as the size of the classifier, ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/08G06N3/12
CPCG06N3/08G06N3/126G06F18/214G06F18/24
Inventor 汪湘湘朱非白郝文平冯坤王勇
Owner ANHUI RONDS SCI & TECH INC CO
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