Abstract
In order to better characterize the performance degradation trend of rolling bearings, a new performance degradation evaluation method based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed to evaluate the performance degradation of rolling bearings in this paper. In the PAPRKM method, the time-domain and frequency-domain features of the vibration signal are extracted to construct the high-dimension feature matrix. Then the PCA is used to reduce the dimension of the feature matrix in order to represent the running state and the declining trend of rolling bearings, so as to eliminate the redundancy and information conflict among these features. Nextly, the PSR is adopted to obtain more relevant information from the time series. By determining the delay time and embedding dimension, the time series are reconstructed to obtain a new performance degradation index, which is regarded as the input data to input into KELM, and the degradation trend prediction model is established to realize the performance degradation trend prediction. Finally, the actual vibration signals of rolling bearings are applied to prove the effectiveness of the PAPRKM. The obtained experimental results show that the PAPRKM method can effectively predict the performance degradation trend of rolling bearings. The predicted results are more accurate than the other compared methods.
Highlights
As one of the most widely used parts in mechanical system, the operation condition of rolling bearings directly affects the whole performance of mechanical system
It shows that the feature index can better describe the performance degradation trend of rolling bearings after the phase space reconstruction, which reflects the accuracy of the phase space reconstruction in establishing the performance degradation index of rolling bearings and the superiority of the phase space reconstruction in the performance degradation evaluation of rolling bearings
In this paper, in order to better characterize the performance degradation trend of rolling bearings, a new method of performance degradation evaluation for rolling bearings based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed based on the analysis of feature indexes in time-domain and frequency-domain of the whole life cycle data of rolling bearings
Summary
As one of the most widely used parts in mechanical system, the operation condition of rolling bearings directly affects the whole performance of mechanical system. The time-domain and frequency-domain feature indexes of bearing vibration signals are extracted as the input of state prediction model to predict the life trend of rolling bearings [3]. The kurtosis is used as the degradation performance index to establish a rolling bearing residual life prediction model based on SVM [4]. The most of the existing feature extraction methods are based on vibration signals, the features of timedomain and frequency-domain are extracted, and a single time-domain index is selected as the feature vector. When there are more features, the feature redundancy may occur, which will result in certain interference In view of these problems, the rationality of feature extraction of time-domain and frequency-domain, and the influence of redundancy and correlation between multi-feature indexes on the accuracy of bearing performance degradation evaluation are considered in this paper. The PAPRKM method can effectively and accurately evaluate the performance degradation process
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
