Multi-dimensional data fault decision-making method and device and storage medium

Abstract

The invention discloses a multi-dimensional data fault decision-making method and a device and a storage medium, and the method comprises the steps: obtaining different monitoring data of equipment, and extracting a feature value; performing fuzzy processing on the extracted characteristic values; performing fuzzy reasoning on the processed characteristic value according to a preset fault rule base to generate a decision result; and defuzzifying the generated decision result to obtain a final fault decision result and a maintenance suggestion. Therefore, objectivity and correctness of decision results can be guaranteed, fault information is fully utilized, fault positions and fault reasons are accurately identified, and maintenance suggestions are provided in time.

Description

technical field [0001] The invention relates to the field of mechanical equipment state monitoring, in particular to a multi-dimensional data fault decision-making method, device and storage medium. Background technique [0002] In the field of mechanical equipment condition monitoring, fault diagnosis of different types of mechanical equipment can be carried out according to the monitoring data, among which the monitoring of bearings is the most common. Bearings are key components in all mechanical transmission systems, and their fault status directly affects the working status of the equipment. [0003] With the rapid development of engines, the working conditions of engine bearings are becoming increasingly harsh and complex. Aero-engine bearings not only work under high-speed, high-temperature and high-load conditions, but also have drastic changes in working conditions, and are prone to failure during the working process. Once the aero-engine bearing fails, it will dir...

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Problems solved by technology

The vibration monitoring method has many practical applications in the fault diagnosis of rolling bearings, but the vibration sensor is generally installed far away from the bearing installation position, which has the disadvantages of large signal transmission attenuation, weak signal, and difficult feature signal extraction

Acoustic emission monitoring has a certain sensitivity to early fault diagnosis, but it is difficult in signal processing and interpretation, which limits the application of acoustic emission monitoring in mechanical fault diagnosis.

[0005] The currently used engine condition monitoring and fault diagnosis methods not only have a single extraction feature, but also a single reasoning mode, which cannot make full use of fault information, and there are problems such as difficulty in obtaining fault knowledge. The level of systematization and intelligence is low, and the diagnostic accuracy is not high. Locate the fault location and find the cause of the fault

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