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

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...

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 engineers may get a wrong description of the domain knowledge in the communication with domain experts

When the reasoning machine uses some subjective knowledge to reason, it is likely to get unreasonable or even wrong results

Therefore, the automatic acquisition of knowledge has become a "bottleneck" restricting the development of intelligent diagnosis

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