Method for predicting bearing fault based on Gaussian process regression

Abstract

The invention discloses a method for predicting a bearing fault based on Gaussian process regression. The method comprises the following five steps of: step 1, setting prediction system parameters, initializing a Gaussian process regression model; step 2, collecting a bearing vibration signal regularly, extracting characteristics of a vibration signal to obtain time domain characteristic parameters of the bearing vibration signal, and carrying out fault symptom judgment; step 3, judging whether a fault symptom exists; step 4, calculating and storing the characteristic parameters, and carrying out dynamic updating of the Gaussian process regression model; and step 5, predicting the fault of a bearing. According to an actual use condition of a product, small amount of data is collected, time that the product possibly has the fault is given quantificationally, a calculation speed and prediction accuracy are improved by using the Gaussian process regression, a whole life cycle of the bearing is divided into three time ranges, such as a health time range, a sub-health time range and a fault time range by use of an idea of health management, fault prediction is carried out in the sub-health state, usage management capacity of the bearing is improved.

Description

Technical field: [0001] The invention relates to a bearing fault prediction method based on Gaussian process regression, and belongs to the technical field of bearing fault prediction. Background technique: [0002] Bearing is an indispensable part of rotating machinery, and it is also a core component to ensure the accuracy, performance, life and reliability of important equipment and facilities such as precision machine tools, high-speed railways, and wind turbines. At the same time, bearings are vulnerable parts, so their condition monitoring , fault diagnosis, and fault prediction have always been research hotspots. In recent years, the condition-based maintenance of the system has gradually attracted people's attention. As the core technology of condition-based maintenance, fault prediction technology has great significance for improving production safety, reducing production costs and prolonging the service life of equipment. [0003] At present, in the fault predicti...

AI Technical Summary

Problems solved by technology

Bearing vibration is characterized by nonlinearity and ambiguity, making it difficult to establish an accurate mathematical model

Moreover, the failure of bearings has individual differences. The actual life of the bearing and the failure mode are different under different working conditions. The existing methods cannot solve this problem well.

In addition, existing methods, such as neural networks, perform unsatisfactorily in terms of prediction accuracy, time-consuming, etc.

Images