No abstract

Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliability and safety of a mechanical system. When local faults happen in a rolling bearing, the complexity of intrinsic oscillations of the vibration signals will change. Refined composite multiscale dispersion entropy (RCMDE) can quantify the complexity of time series quickly and effectively. To measure the complexity of intrinsic oscillations at different time scales, adaptive sparest narrow-band decomposition (ASNBD), as an improved adaptive sparest time frequency analysis (ASTFA), is introduced in this paper. Integrated, the ASNBD and RCMDE, a novel-fault diagnosis-model is proposed for a rolling bearing. Firstly, a vibration signal collected is decomposed into a number of intrinsic narrow-band components (INBCs) by the ASNBD to present the intrinsic modes of a vibration signal, and several relevant INBCs are prepared for feature extraction. Secondly, the RCMDE values are calculated as nonlinear measures to reveal the hidden fault-sensitive information. Thirdly, a basic Multi-Class Support Vector Machine (multiSVM) serves as a classifier to automatically identify the fault type and fault location. Finally, experimental analysis and comparison are made to verify the effectiveness and superiority of the proposed model. The results show that the RCMDE value lead to a larger difference between various states and the proposed model can achieve reliable and accurate fault diagnosis for a rolling bearing.

No abstract

Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.