Rolling bearing performance degradation evaluation method based on HWPSO-SVDD model

Abstract

The invention relates to a rolling bearing performance degradation evaluation method based on an HWPSO-SVDD model, and the method comprises the following steps: S1, obtaining a feature index of a normal rolling bearing vibration signal, obtaining a fusion feature index through a PCA algorithm, and building a training sample; S2, optimizing a penalty parameter C and a nuclear parameter delta of theSVDD model by utilizing an HWPSO optimization algorithm; S3, constructing an HWPSO-SVDD model by using the penalty parameter C and the kernel parameter delta obtained by optimization; S4, inputting the training sample into an HWPSO-SVDD model for model training to obtain a trained performance degradation evaluation model; S5, acquiring a fusion characteristic index of the vibration signal of therolling bearing to be tested, and calculating to obtain a performance degradation index Vc value of the rolling bearing to be tested by using the trained performance degradation evaluation model; andS6, setting a self-adaptive alarm threshold, and when the Vc value exceeds the self-adaptive alarm threshold, carrying out early warning on the early degradation state of the bearing, so that the method has the advantages of improving prediction accuracy and robustness and the like.

Description

technical field [0001] The invention relates to the field of rolling bearing performance evaluation, in particular to a rolling bearing performance degradation evaluation method based on the HWPSO-SVDD model. Background technique [0002] Mechanical equipment is used in all aspects of human life, work and production, and plays a pivotal role in it. At present, machinery and equipment are developing towards large-scale, high-speed, precision, systematization, continuity and automation. The operating environment of machinery and equipment is becoming more and more complex and changeable, which poses new challenges for the health management of equipment. . With the operation of equipment, problems such as aging of parts, reduced reliability, and reduced remaining life gradually appear. Whether the equipment can continue to work safely and efficiently, and whether maintenance can be performed in a timely and effective manner, urgently needs to be resolved by humans. Once the e...

AI Technical Summary

Problems solved by technology

However, the parameter optimization of the current SVDD model is prone to fall into the defect of local optimum, or the prediction result of the model is inaccurate due to the blindness of artificial selection of model parameters.

Images