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Feature construction method and system for predictio maintenance based on convolution operator

A construction method and predictive technology, applied in the field of machine learning, can solve problems such as data acquisition cost reduction, achieve good promotion and application value, and enhance the effect of robustness

Pending Publication Date: 2020-05-15
INSPUR ARTIFICIAL INTELLIGENCE RES INST CO LTD SHANDONG CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

With the popularization and application of sensors, the cost of data acquisition has been greatly reduced

Method used

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Embodiment

[0029] The convolution operator-based feature construction method for predictive maintenance of the present invention includes the following steps:

[0030] S1. Preprocessing the data collected by the sensor to obtain a data matrix.

[0031] The data collected by the sensor is preprocessed to obtain a data matrix with the same row and the same column.

[0032] S2. Setting the convolution kernel.

[0033] Setting the convolution kernel includes setting the size of the convolution kernel, the number of convolution kernels, the convolution step size and whether to perform data padding.

[0034] S3. Perform a convolution operation on the data matrix and the set convolution kernel to obtain a new feature matrix. When there are multiple convolution kernels, multiple new feature matrices are obtained.

[0035] S4. Concatenate the new feature matrices to obtain a new set of feature matrices. If the new feature matrix is ​​consistent with the original data matrix, it can be concate...

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Abstract

The invention discloses a feature construction method and system for predictive maintenance based on a convolution operator, and belongs to the technical field of machine learning. The method comprises the following steps: S1, preprocessing data acquired by a sensor to obtain a data matrix; S2, setting a convolution kernel; S3, performing convolution operation on the data matrix and a set convolution kernel to obtain a new feature matrix; S4, connecting the new feature matrixes in series to obtain a new feature matrix set; S5, employing machine learning for the new feature matrix, and performing anomaly detection. According to the invention, through convolution of the convolution kernel and sensor data, the robustness of the features is enhanced, better features are obtained for predictivemaintenance, and the method has very good popularization and application values.

Description

technical field [0001] The invention relates to the technical field of machine learning, and specifically provides a convolution operator-based feature construction method and system for predictive maintenance. Background technique [0002] Predictive maintenance is one of the key technologies in smart manufacturing. Intelligent predictive maintenance of equipment is an inevitable equipment maintenance method adopted by modern intelligent manufacturing enterprises, and the result judgment of predictive maintenance through data means is the only way for modern digital enterprises. Its realization is mainly realized through data collection, machine learning modeling and computing power support. The data is mainly generated by various sensors installed on the equipment, and after processing and processing, it forms useful data that can be used, and these data express the health status of the equipment. With the popularization and application of sensors, the cost of data acqui...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 李锐段强安程治金长新
Owner INSPUR ARTIFICIAL INTELLIGENCE RES INST CO LTD SHANDONG CHINA
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