GPU-based equipment fault early-warning and diagnosis method for improving weighted association rules

A technology of equipment failure and weighted correlation, applied in the direction of instruments, adaptive control, control/regulation systems, etc., can solve problems such as unreasonableness, errors, and incorrect description of domain knowledge

Inactive Publication Date: 2010-09-01
天津开发区精诺瀚海数据科技有限公司
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AI Technical Summary

Problems solved by technology

However, in the traditional expert system, the acquisition of knowledge is the result of the collaborative work of domain experts and software engineers. There are two main problems in this link: first, it is difficult to convert the descriptions of domain experts into rule knowledge; Due to the deviation of understanding, software engine...

Method used

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  • GPU-based equipment fault early-warning and diagnosis method for improving weighted association rules
  • GPU-based equipment fault early-warning and diagnosis method for improving weighted association rules
  • GPU-based equipment fault early-warning and diagnosis method for improving weighted association rules

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Embodiment Construction

[0059] The technical solution of the present invention will be further described below in conjunction with the equipment of a steel company.

[0060] The large-scale forging system of a steel company is mainly composed of three equipments A, B, and C. A large amount of previous operation data (including normal state and fault state), including the degree of vulnerability (equipment damage times), Failure class, criticality given by experts and sampling points on three devices: temperature, pressure, vibration, rotational speed and cause of failure.

[0061] The implementation steps are as follows:

[0062] 1) First build the RARG model based on the fast association weighting rule algorithm of the image processor GPU, and the construction method is as described in the technical scheme;

[0063] 2) Obtain sample data of temperature, pressure, vibration, speed, vulnerability (equipment damage times), fault level and fault cause from the database, and clean and remove inconsisten...

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Abstract

The invention relates to a GPU-based equipment fault early-warning and diagnosis method for improving weighted association rules, which belongs to the field of equipment fault diagnosis and early warning. The method comprises the following steps of: constructing a graphic processing unit (GPU)-based RARG model for realizing a quick weighted association rule algorithm; mining historical monitoring data of the equipment by utilizing a GPU-based improved weighted association rule model, and constructing an association rule pattern base; monitoring the equipment data, and extracting the eigenvalue; judging whether the eigenvalue reaches the threshold, if so, determining that the equipment is in a fault state, and otherwise, determining that the equipment is in a non-fault state; if the equipment is in a non-fault state, matching related data with the association rule pattern base, if the matching succeeds, determining that the equipment is in a defect state, namely, the equipment has a potential fault, and if the matching does not succeed, returning to the step of data monitoring. The invention develops a GPU-based RARG model for realizing the quick weighted association rule algorithm, which has an important application value.

Description

technical field [0001] The invention belongs to the field of early warning and diagnosis of equipment failure, and relates to a method for early warning and diagnosis of equipment failure based on GPU-based improved weighted association rules. Background technique [0002] With the increasing size and complexity of modern production equipment, the phenomenon of equipment failure is also increasing, and the losses are also increasing. Therefore, it is very important to conduct real-time online monitoring of the equipment system, conduct equipment failure mechanism research, establish effective and accurate fault diagnosis and early warning modes, and optimize the operation of the system. [0003] At present, the fault diagnosis method based on knowledge of expert system is widely used in the field of equipment fault diagnosis. The expert system is mainly composed of knowledge base, inference engine and user interface. The knowledge base is the memory of expert knowledge, ex...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 刘晶朱清香
Owner 天津开发区精诺瀚海数据科技有限公司
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