Equipment fault auxiliary diagnosis method and system for manufacturing industry

A technology for equipment failure and auxiliary diagnosis, applied in the field of machine learning, can solve the problems of high labor cost, large personnel dependence, long fault diagnosis time, etc., and achieve the effect of low labor cost, short fault diagnosis time, and reduced labor cost.

Active Publication Date: 2020-10-16
HANGZHOU WEIMING XINKE TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems of high labor costs, long fault diagnosis time, and excessive reliance on personnel in the existing fault diagnosis schemes of manufacturing equipment, the present invention provides a manufacturing-oriented equipment fault auxiliary diagnosis method and system

Method used

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  • Equipment fault auxiliary diagnosis method and system for manufacturing industry
  • Equipment fault auxiliary diagnosis method and system for manufacturing industry
  • Equipment fault auxiliary diagnosis method and system for manufacturing industry

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Experimental program
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Embodiment 1

[0028] Such as figure 1 , 2 As shown, in order to solve at least one problem existing in the prior art, this embodiment can provide a manufacturing-oriented auxiliary equipment fault diagnosis method, which can realize real-time monitoring of equipment operation status and efficient auxiliary diagnosis of equipment faults. The method includes, but is not limited to, the following procedures.

[0029] First, collect historical real-time operating data of the equipment to be diagnosed at a set frequency. The set frequency can be set according to actual needs, such as 0.1 Hz, etc. In this embodiment, real-time data from different sensors of the device can be obtained every 10 s according to the difference of the device. For example, various sensors can be used to collect corresponding real-time operation data of the machine, such real-time operation data includes but not limited to pressure, temperature, rotational speed, etc. During implementation, pressure data can be collect...

Embodiment 2

[0051] Such as figure 2 As shown, based on the same inventive concept as that of Embodiment 1, this embodiment provides a manufacturing-oriented equipment fault auxiliary diagnosis system. The framework of the entire equipment fault auxiliary diagnosis system can include three parts: data acquisition part, multi-level feature prediction model part and abnormal detection part based on active learning. The multi-level feature prediction model part can include two-level prediction. The idea comes from integrated learning stacking (stacked) framework, divided into a base learning part and a meta learning part. Specifically, the auxiliary fault diagnosis system may include, but not limited to, a state prediction module, a state monitoring module, a fault judgment module, and a troubleshooting reference module.

[0052] The state prediction module is used to generate the predicted value of the operation state of the equipment to be diagnosed. The state prediction module includes ...

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Abstract

The invention discloses a manufacturing industry-oriented equipment fault auxiliary diagnosis method and system. The method comprises but is not limited to the following processes of generating a predicted value of an operation state of to-be-diagnosed equipment; monitoring the real value of the operation state of the to-be-diagnosed equipment in real time; and judging whether the to-be-diagnosedequipment has a fault based on the difference between the predicted value and the true value. The system comprises but is not limited to a state prediction module, a state monitoring module and a fault judgment module. The state prediction module is used for generating a predicted value of the running state of the to-be-diagnosed equipment, the state monitoring module is used for monitoring a realvalue of the running state of the to-be-diagnosed equipment in real time, and the fault judgment module is used for judging whether the to-be-diagnosed equipment fails based on the difference betweenthe predicted value and the real value. The method can effectively assist maintenance personnel in judging whether the monitored equipment fails or not, has the advantages of short fault diagnosis time, low labor cost, small dependence on personnel experience and technical level and the like, and further can improve the equipment operation and maintenance efficiency and production benefits of themanufacturing industry.

Description

technical field [0001] The present invention relates to the technical field of machine learning, and more specifically, the present invention relates to a manufacturing-oriented equipment fault auxiliary diagnosis method and system. Background technique [0002] At present, more and more manufacturing enterprises have begun to implement digitalization, automation and intelligent transformation, so as to improve manufacturing efficiency and quality, and reduce operating and labor costs. It can be seen that a high-stability and high-reliability manufacturing system will be an important guarantee for enterprises to improve production efficiency. [0003] Fault diagnosis of equipment in manufacturing enterprises often relies on manual inspection and maintenance. Inspection personnel need to regularly inspect the machine every day, and report to maintenance personnel if they find faults. Factory director, etc. Obviously, the whole process of manual patrol inspection consumes a ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F17/18G06N20/00G01M13/00
CPCG06F17/18G06N20/00G01M13/00G06F18/24155G06F18/253G06F18/214
Inventor 王尔昕唐宁赵鑫安陈曦麻志毅
Owner HANGZHOU WEIMING XINKE TECH CO LTD
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