Abstract
Due to the relatively weak early fault characteristics of rolling bearings, the difficulty of early fault detection increases. For unsolving this problem, an incipient fault detection method based on deep empirical mode decomposition and principal component analysis (Deep EMD-PCA) is proposed. In this method, multiple data processing layers are created to extract weak incipient fault features, and EMD is used to decompose the vibration signal. This method establishes an accurate data mode, which can improve the incipient fault detection capability. It overcomes the difficulties of incipient fault detection, in which weak fault features can be extracted from the background of strong noise. From a theoretical point of view, this paper proves that the Deep EMD-PCA method can retain more variance information and has a good early fault detection ability. The experiment results indicate that the detection rate of Deep EMD-PCA is about 85%, and the failure detection delay time is almost zero. The incipient faults of rolling element bearings can be detected accurately and timely by Deep EMD-PCA. The method effectively improves the accuracy and timeliness of fault detection under actual working conditions and has good practical application value.
Highlights
Rolling element bearings are one of the common mechanical components in rotating machinery, and their operating conditions often directly affect the performance of the entire machine [1,2,3]
Experimental Results and Analysis is section uses the experimental data of Case Western Reserve University and the self-built mechanical failure comprehensive simulation testbed to collect the failure vibration data of the inner and outer rings of the rolling bearing. e principal component analysis (PCA)-Support Vector Machine (SVM) fault diagnosis method first reduces the dimensionality of the data through PCA and puts the later data into the SVM model for classification. is method has advantages in processing small samples and is not sensitive to noise data and feature vectors
Comparing the empirical mode decomposition (EMD)-PCA method and PCA-SVM method with the Deep EMD-PCA method proposed in this paper, it proves the feasibility of the early fault detection method proposed in this paper
Summary
Rolling element bearings are one of the common mechanical components in rotating machinery, and their operating conditions often directly affect the performance of the entire machine [1,2,3]. E main challenges of incipient fault detection for rolling element bearings are as follows [7, 8]:. During the running process of the rolling element bearings, there is a large amount of amplitude interference noise in the signal (2) Due to the harsh operating environment and limited field measurement conditions, the obtained signal has an impact interference signal ere are many incipient fault detection methods for rolling element bearings, which can be roughly divided into two methods based on model-driven and data-driven methods [9]. E data collected by the system is analyzed by data-driven methods, to achieve the effect of incipient fault detection [12,13,14] Data-driven methods can effectively overcome these problems. e data collected by the system is analyzed by data-driven methods, to achieve the effect of incipient fault detection [12,13,14]
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.
