A machine learning-based equipment fault detection method

A technology of equipment failure and detection method, which is applied in the direction of instruments, computer parts, structured data retrieval, etc., can solve the problems of not being widely applicable to data types, low degree of automation, and inability to automatically detect, so as to prevent the occurrence of mechanical failures, The effect of improving economic benefits and improving production efficiency

Active Publication Date: 2020-11-24
成都航天科工大数据研究院有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the current process of equipment maintenance, it is usually to wait until the equipment reports a fault or finds that it is not working properly, and then manually check and verify the industrial equipment in real time. This manual detection method cannot automatically detect the faults of industrial equipment. Detection, low degree of automation
[0003] In the existing technology, there is rolling bearing fault detection based on machine learning, but this technology is mainly aimed at analyzing various characteristic data and the information represented by rolling bearings in the production process, so it cannot be widely applied to various processing Data types of equipment such as machine tools to ensure the safe production of equipment

Method used

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  • A machine learning-based equipment fault detection method

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Embodiment 1

[0039] Such as figure 1 As shown, the present embodiment provides a machine learning-based device fault detection method, comprising the following steps:

[0040] S1. Acquiring the collected initial data of the faulty equipment;

[0041] S2. Perform a cleaning operation on the initial data, and perform secondary screening on the initial data after the cleaning operation is completed to obtain pre-data;

[0042] S3. Optimizing the pre-data by genetic algorithm, randomly generating multiple starting points from the pre-analyzed data, and then retrieving the starting points at the same time, and outputting the retrieval results;

[0043] S4. Weight the retrieval results by AdaBoost meta-algorithm, and classify the data to obtain the initial result set;

[0044] S5. Classify the initial result set successively according to the set weight of the samples in each classifier, so as to obtain the weighted average result;

[0045] S6. By comparing the weighted average result and the ...

Embodiment 2

[0049] On the basis of Example 1, such as figure 1 As shown, the present embodiment provides a machine learning-based device fault detection method, comprising the following steps:

[0050] S1. Acquiring the collected initial data of the faulty equipment;

[0051] S2. Perform a cleaning operation on the initial data, and perform secondary screening on the initial data after the cleaning operation is completed to obtain pre-data;

[0052]S3. Optimizing the pre-data by genetic algorithm, randomly generating multiple starting points from the pre-analyzed data, and then retrieving the starting points at the same time, and outputting the retrieval results;

[0053] S4. Weight the retrieval results by AdaBoost meta-algorithm, and classify the data to obtain the initial result set;

[0054] S5. Classify the initial result set successively according to the set weight of the samples in each classifier, so as to obtain the weighted average result;

[0055] S6. By comparing the weight...

Embodiment 3

[0060] On the basis of Example 1, such as figure 1 As shown, the present embodiment provides a machine learning-based device fault detection method, comprising the following steps:

[0061] S1. Acquiring the collected initial data of the faulty equipment;

[0062] S2. Perform a cleaning operation on the initial data, and perform secondary screening on the initial data after the cleaning operation is completed to obtain pre-data;

[0063] S3. Optimizing the pre-data by genetic algorithm, randomly generating multiple starting points from the pre-analyzed data, and then retrieving the starting points at the same time, and outputting the retrieval results;

[0064] S4. Weight the retrieval results by AdaBoost meta-algorithm, and classify the data to obtain the initial result set;

[0065] S5. Classify the initial result set successively according to the set weight of the samples in each classifier, so as to obtain the weighted average result;

[0066] S6. By comparing the weigh...

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Abstract

The invention belongs to the technical field of data collection and analysis of industrial equipment, and discloses a machine learning-based equipment fault detection method. The technical scheme of the present invention is: S1. Acquire the initial data of the equipment that has failed; S2. Perform cleaning operations and secondary screening on the initial data; S3. Perform optimization operations on the pre-data and output retrieval results; S4. Classify to obtain the initial result set; S5. Classify the initial result set one by one; S6. By comparing the weighted average result and the initial result set, judge whether the equipment fault represented by the initial data belongs to a known fault; S7. Output through the man-machine interface Fault information or add the initial data to the fault database. The invention can realize the self-learning function of the machine, and at the same time, establishes the fault database of the equipment, can accurately and timely determine the equipment fault and the solution, can reduce the maintenance times of the mechanical equipment, significantly improve the maintenance efficiency, and is suitable for popularization and use.

Description

technical field [0001] The invention belongs to the technical field of data collection and analysis of industrial equipment, and in particular relates to a machine learning-based equipment fault detection method. Background technique [0002] In the process of industrial production, equipment maintenance is an essential work. However, in the current process of equipment maintenance, it is usually to wait until the equipment reports a fault or finds that it is not working properly, and then manually check and verify the industrial equipment in real time. This manual detection method cannot automatically detect the faults of industrial equipment. Detection, low degree of automation. [0003] In the existing technology, there is rolling bearing fault detection based on machine learning, but this technology is mainly aimed at analyzing various characteristic data and the information represented by rolling bearings in the production process, so it cannot be widely applied to var...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/2458G06F16/22G06F16/215G06K9/62
CPCG06F18/254
Inventor 祝守宇张辉熊楗洲刘勇王开业樊妍睿马波涛朱芝濡
Owner 成都航天科工大数据研究院有限公司
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