Equipment fault diagnosis method and device based on multi-sensor data fusion

Abstract

The invention provides an equipment fault diagnosis method and device based on multi-sensor data fusion. The method comprises the steps of constructing a fault identification framework of equipment according to a predetermined fault mode; acquiring equipment operation state data acquired by a plurality of sensors, and converting the equipment operation state data into digital signals; performing fault feature extraction on the digital signal by using a pre-constructed adaptive fault diagnosis model, and calculating a BPA value of each fault mode by using a basic probability assignment method according to an extracted fault feature matrix to obtain a primary diagnosis evidence body of the equipment; and comparing the DS theoretical conflict factor with a preset threshold, and selecting a corresponding fusion rule according to a comparison result to perform decision fusion on the primary diagnosis evidence body to obtain a fault diagnosis result of the equipment. According to the invention, multi-source fault signals can be fused to carry out equipment state monitoring and intelligent diagnosis, so that the equipment fault diagnosis efficiency and accuracy are effectively improved.

Description

technical field [0001] The present invention relates to the technical field of equipment fault diagnosis, in particular to a multi-sensor data fusion equipment fault diagnosis method, device, terminal equipment and readable storage medium. Background technique [0002] Manufacturing is the main body of the national economy and the foundation of a strong country. The healthy operation of mechanical equipment is the premise to ensure the smooth operation of the production line, the premise of the vigorous development of the manufacturing industry, and an important construction content of the intelligent manufacturing project. Mechanical equipment condition monitoring and intelligent diagnosis are effective ways to ensure the healthy operation of mechanical equipment. Monitoring and diagnosing the operating status of mechanical equipment has great practical significance. It can not only detect hidden troubles early, repair them in time, make full use of the effective service l...

AI Technical Summary

Problems solved by technology

Third, most of the traditional fault diagnosis methods rely on expert knowledge, and the feature extraction of the original signal by manual means is inefficient and difficult to deal with the rapid growth of massive data

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