Fault diagnosis method and device of rolling bearing of low-speed heavy-duty equipment and medium

Abstract

The invention provides a fault diagnosis method and device of a rolling bearing of low-speed heavy-duty equipment and a medium. The method comprises a step of acquiring a vibration signal of the rolling bearing of the low-speed heavy-duty equipment and analyzing the vibration signal to obtain a plurality of state signals, a step of filtering and denoising the multiple state signals to obtain denoised signals, and a step of constructing a three-dimensional feature on the denoised signals, obtaining a feature vector of the three-dimensional feature, taking a part of the feature vector in the feature vector of the three-dimensional feature as an input, establishing a rolling bearing fault diagnosis model based on a nuclear pole nuclear limit learning machine algorithm, inputting the remainingpart of the feature vector is into the rolling bearing fault diagnosis model to obtain a fault diagnosis result of the rolling bearing of the low-speed heavy-duty equipment. The three-dimensional feature vector based on 'EEMD energy entropy-morphological fractal dimension-morphological spectral entropy' is used an input of the classification model, the working state of the bearing can be accurately characterized and identified, and a good fault state identification effect can be obtained.

Description

technical field [0001] The invention relates to the field of mechanical fault diagnosis, in particular to the field of fault diagnosis method, equipment and medium of rolling bearings of low-speed and heavy-duty equipment. Background technique [0002] Many low-speed and heavy-duty equipment in the metallurgical industry, such as converter tilting mechanism, ladle turret, bucket wheel stacker and reclaimer, and bellless furnace top distributor, are critical equipment in the production process. These equipment are large-scale heavy machinery, which are expensive, have a long spare parts cycle, and are difficult to repair and replace parts. Since it works in a harsh environment of high temperature, high humidity, and much dust for a long time, and often needs to bear hundreds of thousands of tons of work load, the health status of the equipment has an important impact on ensuring the stable operation of production. Although equipment condition monitoring and fault diagnosis t...

AI Technical Summary

Problems solved by technology

Although equipment condition monitoring and fault diagnosis technology based on vibration analysis has gradually been popularized and promoted in industrial applications at home and abroad, there is still a lack of effective fault analysis methods for low-speed and heavy-duty equipment. The fault diagnosis of equipment has limitations such as the difficulty of effectively extracting the fault characteristics under the coexistence of multiple faults, the difficulty of effectively describing the non-stationary change process of equipment fault characteristics, and the lack of multi-scale analysis methods for fault characteristics.

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