Equipment fault diagnosis method, device and system based on multi-source monitoring data fusion

Abstract

The embodiment of the invention discloses an equipment fault diagnosis method, device and system based on multi-source monitoring data fusion. The method comprises the steps of obtaining multi-sourcemonitoring data of target equipment; carrying out data segmentation processing on the multi-source monitoring data based on a plurality of different time window lengths, respectively storing the multi-source monitoring data segmented based on the different time window lengths into different data sets, and obtaining a multi-source monitoring data set corresponding to each time window length; respectively carrying out feature extraction on the segmented multi-source monitoring data in each multi-source monitoring data set to obtain multi-source feature data corresponding to the corresponding time window length; performing first fusion processing on the multi-source feature data corresponding to each time window length to obtain multi-time window fusion data; and performing fault diagnosis onthe target device by using the multi-time window fusion data to obtain a fault diagnosis result of the target device. Therefore, the accuracy of fault diagnosis of the target equipment can be greatlyimproved.

Description

technical field [0001] This specification relates to the technical field of equipment fault diagnosis, and in particular, relates to a method, device and system for equipment fault diagnosis based on multi-source monitoring data fusion. Background technique [0002] Motor-driven equipment is widely deployed in various scenarios in the industry and is the main device driving various important production activities. As an important part of the production system, the safe and reliable operation of motor-driven equipment is the basis for continuous and stable production. Once the motor-driven equipment fails, it will affect the overall performance of the production equipment, affect production efficiency, cause economic losses, and even cause catastrophic accidents in severe cases. Therefore, it is of great significance to ensure the safe operation of the production system to carry out early fault diagnosis and traceability of motor-driven equipment through monitoring informati...

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Problems solved by technology

[0004] When processing fused data, it is usually necessary to segment the data with a fixed-length time window for subsequent analysis and identification, but it is usually difficult to determine the optimal time window length in practical applications

At the same time, even if the optimal time window length can be determined, for different equipment, different failure modes, and different parameter types, it is difficult to extract characteristic data that accurately characterizes different failure modes of different equipment based on a single time window length. Thus affecting the accuracy of equipment fault diagnosis and the universality of diagnostic methods

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