Model training method, training device, electronic equipment and computer readable medium

A technology for model training and variance inflation factor, applied in computing, computing models, machine learning and other directions, can solve the problems of complex, low-cost, and resource-intensive data sets, and achieve the effect of improving training efficiency

Inactive Publication Date: 2022-03-11
ALIBABA (CHINA) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in practice, the data sets generated based on high-frequency vibration signals are complex and diverse. If these data sets are directly used for model training, it will undoubtedly consume a lot of resources and be inefficient.

Method used

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  • Model training method, training device, electronic equipment and computer readable medium
  • Model training method, training device, electronic equipment and computer readable medium
  • Model training method, training device, electronic equipment and computer readable medium

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Embodiment Construction

[0058] The present disclosure is described below based on examples, but the present disclosure is not limited only to these examples. In the following detailed description of the disclosure, some specific details are set forth in detail. The present disclosure can be fully understood by those skilled in the art without the description of these detailed parts. In order to avoid obscuring the essence of the present disclosure, well-known methods, procedures, and procedures are not described in detail. Additionally, the drawings are not necessarily drawn to scale.

[0059] The model training method provided by the embodiment of the present disclosure is as follows: figure 1 shown.

[0060] Step S11 is to construct an initial data set of high-frequency vibration signals.

[0061] Step S12 is to filter the initial data set to obtain the key data set, that is, perform feature screening on the initial data set containing rich features, and filter out a set of features with high i...

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Abstract

The invention provides a model training method, a training device, electronic equipment and a computer readable medium. The method comprises the following steps: constructing an initial data set of high-frequency vibration signals; screening the initial data set to obtain a key data set; the key data set is adopted to carry out model training to obtain a fault classification model, the initial data set is represented by three dimensions of a fault category, a sample and a feature, and the screening comprises the following steps: for each feature, carrying out average distance calculation on the two dimensions of the sample and the fault category respectively, the representation value of the importance of each feature is obtained based on the average distance; and selecting the features of which the characterization values of importance are greater than a first threshold value to form a key data set. According to the embodiment of the invention, the characterization value of the importance of each feature is calculated and determined based on the average distance of the two dimensions of the sample and the fault category, the initial data set is screened according to the characterization value of the importance, and the screened features are used for model training, so that the model training efficiency is improved.

Description

technical field [0001] The present disclosure relates to the field of combining artificial intelligence with machine fault monitoring and diagnosis, and in particular to a model training method, a training device, electronic equipment and a computer-readable medium. Background technique [0002] Due to structural and processing installation defects of rotors, bearings, housings, seals and foundations, or due to external effects, a large number of industrial equipment vibrates during operation, and excessive vibration is often the main cause of equipment damage. According to statistics, for the large-scale and wide-ranging rotating machinery and reciprocating machinery in the industry, the failure of equipment due to vibration accounts for more than 60% of the total failure rate. Therefore, the vibration monitoring and analysis of mechanical equipment is very important. Compared with other state parameters, such as the temperature, pressure, flow rate of lubricating oil or in...

Claims

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Application Information

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Patent Type & AuthorityApplications(China)
IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor陈曦王巍巍葛成王明
OwnerALIBABA (CHINA) CO LTD