Bearing performance degradation state detection method and system

Abstract

The invention discloses a bearing performance degradation state detection method and system. The method comprises the steps: S1, collecting vibration signals in the full-life service process of a bearing; S2, constructing a high-dimensional degradation trend feature set, and performing smoothing processing by using exponentially weighted moving average; S3, designing a feature sensitivity evaluation criterion, and screening out a bearing performance degradation state sensitive feature set; and S4, fusing the sensitive feature set by using a consistent manifold approximation algorithm, and further smoothing the fusion index by using exponential weighted moving average to form a bearing performance degradation state curve. According to the method, noise in characterization indexes can be removed, and fluctuation in the performance degradation process of the bearing is slowed down; monotonicity and correlation in the bearing performance degradation process are integrated in the characterization index screening criterion, and effective characterization indexes can be selected more reasonably; and consistent manifold approximation algorithm is used for fusing selected effective indexes, the global structure and the local structure of data can be considered, and the defects of a traditional data fusion method are overcome.

Description

technical field [0001] The invention relates to the technical field of mechanical equipment health monitoring, in particular to a method and system for detecting the degradation state of bearing performance. Background technique [0002] Due to the development of sensor and computer technology, a large amount of condition monitoring data has been accumulated in industrial production. Data-driven methods use condition monitoring data to characterize the degradation state of bearings, rather than building a specific model that is not easily available, and have been widely used for bearing life prediction. Generally, data-driven bearing life prediction includes three steps: data collection, health indicator construction and remaining life prediction. Among them, the health index is to evaluate the degradation state of the bearing in service by extracting features from the collected vibration data, so as to quantify its historical degradation process. Therefore, the quality of...

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Problems solved by technology

However, the current traditional data fusion method is difficult to take into account the global structure and local structure of the bearing degradation state, and cannot fully describe the degradation state of the bearing, which also brings certain complexity to the follow-up bearing life prediction work

In general, the traditional method has the following three shortcomings: (1) The vibration signal of the bearing contains a lot of noise and random fluctuations. These harmful information will affect the trend of the bearing index curve, which is not conducive to the description of the degradation state of the bearing

(3) The traditional fusion dimensionality reduction method cannot take into account the global structure and local structure of the bearing degradation state data

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