An equipment fault diagnosis method and device based on cloud service

Abstract

The invention discloses an equipment fault diagnosis method and device based on cloud service, and the method comprises the steps: S1, obtaining a fault diagnosis symptom vector which comprises the operation data of equipment of an automatic verification system in real time based on a cloud storage technology; S2, obtaining a fault vector through fuzzy reasoning operation according to the obtainedfault diagnosis symptom vector; and S3, inputting the fault vector into the trained ELM secondary fault diagnosis model for secondary fault diagnosis to obtain a fault type diagnosis result. The datamanagement performance based on the cloud service is combined with the two-stage fault diagnosis decision-making model composed of fuzzy reasoning and ELM, and the technical problem that the equipment fault diagnosis accuracy of a traditional automatic verification center based on the cloud service is low in complex automatic metering detection system environments of multiple sources, multiple dimensions, heterogeneity and the like is solved.

Description

technical field [0001] The present application relates to the field of equipment intelligent management, in particular to a cloud service-based equipment fault diagnosis method and device. Background technique [0002] Extreme Learning Machine (ELM) is a type of machine learning algorithm designed for Single Layer Feedforward Neuron Network (SLFN). Its main feature is that the hidden layer node parameters can be randomly or artificially given. It is fixed and does not need to be adjusted, and the learning process only needs to calculate the output weight. ELM has the advantages of high learning efficiency and strong generalization ability. [0003] The use of a large number of automated verification equipment has improved the verification efficiency of metering equipment on the one hand, but at the same time it has also caused problems such as a surge in the total amount of data related to equipment operation and maintenance, management difficulties, etc., and presents comp...

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Problems solved by technology

[0003] On the one hand, the use of a large number of automated verification equipment has improved the verification efficiency of metering equipment, but at the same time, it has also caused problems such as a surge in the total amount of data related to equipment operation and maintenance, management difficulties, etc., and presents complex characteristics such as multi-source, multi-dimensional, and heterogeneous. Existing fault diagnosis decision-making methods and systems mainly analyze the collected single data and video source information, and it is difficult to achieve high-accuracy intelligent fault diagnosis for equipment, especially for multi-source, multi-dimensional, heterogeneous and other structures. The accuracy of complex and large data information will be further reduced, which leads to the technical problem of low accuracy of existing equipment intelligent fault diagnosis

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