AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
In machining operations, the misalignment of the bearing assembly or imbalanced load often leads to deflection and failure of the tool spindle. The use of single feature information does not accurately monitor the complex working conditions. Considering this, this paper proposes a rolling bearing running condition monitoring method which is based on multiple feature information. Firstly, a multi-dimensional feature matrix is obtained by extracting the features of a single type of raw data in the time domain, frequency domain, and time-frequency domain, and then the dimensionality of the matrix is reduced by principal component analysis (PCA). An entropy weight improved the D-S (EWID-S) evidence theory is proposed. By updating the initial evidence source, and applying the Euclidean distance of the spatial centroid, the fusion results were evaluated. Finally, a test rig for eccentric bearing load operation is developed to obtain the vibration signals at two distinct locations and to confirm the proposed method. The test results show that the condition monitoring method based on the PCA and EWID-S evidence theory can effectively identify the bearing operating at different degrees of deflection. At the same time, by comparing with other improved D-S evidence theory methods, it is verified that this method has more advantages in information fusion and bearing condition monitoring.
Li Z, Yan X. Study on data fusion of multi-dimensional sensors for health monitoring of rolling bearings. Journal of Insight - Non-Destructive Testing and Condition Monitoring 2013; 55(3): 147-151.
Gu R, Chen J, Hong R, et al. Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator. Journal of Measurement 2020; 149: 106941.
Yi J, Pang B, Liu H, et al. Influence of misalignment on nonlinear dynamic characteristics for matched bearings-rotor system. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 2014; 228(2): 172-181.
Li JM, Wang H, Li M, et al. Fault diagnosis of rolling bearing based on improved adaptive no-parameter empirical wavelet transform. Journal of Metrology 2020; 41(6): 710-716 [Chinese].
Gu X, Yang S, Liu Y, et al. Rolling element bearing faults diagnosis based on Kurtogram and frequency domain correlated kurtosis. Journal of Measurement Science and Technology 2016; 27(12): 125019 [Chinese].
Sun W, An Yang G, Chen Q, et al. Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation. Journal of Vibration and Control 2013; 19(6): 924-941 [Chinese].
Li SM, Hou YZ, Li XL. A review of time and frequency domain analysis methods for rolling bearing vibration faults. J. Journal of Chongqing University of Technology (Natural Sciences) 2021; 35(10): 85-93 [Chinese].
Safizadeh MS, Latifi SK. Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Journal of Information Fusion 2014; 18: 1-8.
Wang J, Wang D, Wang S, et al. Fault diagnosis of bearings based on multi-sensor information fusion and 2D convolutional neural network. Journal of IEEE Access 2021; 9: 23717-23725.
Zhou X, Jiang P. Variation source identification for deep hole boring process of cutting-hard workpiece based on multi-source information fusion using evidence theory. Journal of Intelligent Manufacturing 2017; 28(2): 255-270.
Basir O, Yuan X. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Journal of Information Fusion 2007; 8: 379-386.
Wang L, Lv WM, Teng KN. Real-time health state assessment of long-term storage equipment based on test data. Journal of Systems Engi-neering and Electronics Technology 2013, 35(6): 1212-1217.
Hui ZH. Distributed multi-sensor target recognition based on weighted D-S evidence theory. Journal of Computer Applications 2007; (1): 56-57, 97 [Chinese].
Li S, Liu G, Tang X, et al. An ensemble deep convolutional neural net-work model with improved D-S evidence fusion for bearing fault diagnosis. Journal of Sensors 2017; 17(8): 1729 [Chinese].
Ban CL, Li SY, Zhang B. Research on rolling bearing fault diagnosis method based on sample entropy and SVM. Journal of China Testing 2020; 46(11): 37-42 [Chinese].
Yen GG, Lin KC. Wavelet packet feature extraction for vibration monitoring. Journal of IEEE Transactions on Industrial Electronics 2000; 47(3): 650-667.
Rafiee J, Rafiee MA, Tse PW. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Journal of Expert Systems with Applications 2010; 37(6): 4568-4579.
Ngui WK, Leong MS, Hee LM, et al. Wavelet analysis: Mother wavelet selection methods. Journal of Applied Mechanics and Materials 2013; 393: 953-958.
Yadavar NS, Rezaie H, Kilpatrik M, et al. Intelligent fault diagnosis of bearings based on energy levels in frequency bands using wavelet and Support Vector Machines (SVM). Journal of Manufacturing and Materials Processing 2019; 3(1): 11.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
京公网安备11010802044758号