Abstract
Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings.
Highlights
Rolling bearings are important and vulnerable components widely used in rotating machinery.The running state of rolling bearings significantly affects the performance, safety, and reliability of the overall machinery [1,2,3]
An approach for the bearing fault severity monitoring based on the texture feature extraction of sparse time–frequency image (TFI) was proposed
The gray level co-occurrence matrix (GLCM)-based texture features of the sparse TFIs were extracted for an automatic classification to realize fault severity monitoring
Summary
Rolling bearings are important and vulnerable components widely used in rotating machinery. The running state of rolling bearings significantly affects the performance, safety, and reliability of the overall machinery [1,2,3]. The state detection and fault diagnosis for rolling bearings have. Sci. 2018, 8, 1538 important practical significance. As bearing failure is a dynamic process, it is insufficient to know whether there is any fault or the fault type for the preventive maintenance of bearings in engineering applications. Determining the failure evolution process and accurately monitoring the fault severity are paramount for an effective bearing maintenance [4,5,6]. The fault severity monitoring of rolling bearings is becoming increasingly important [7,8]
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.
