The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest
نویسندگان
چکیده
منابع مشابه
Improved Ensemble Empirical Mode Decomposition for Rolling Bearing Fault Diagnosis
Rolling bearing is an important part in mechanical system and faults occur frequently with vibration noise. Empirical mode decomposition (EMD) is a tool for nonlinear and non-stationary signals analysis. However, the major drawbacks of EMD are mode mixing problem, ensemble empirical mode decomposition (EEMD) provides a new tool for signal analysis, and it is an improved technique of EMD. In ord...
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In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
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A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals' separation, the pre...
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A fault diagnosis method for sensor fault based on ensemble empirical mode decomposition (EEMD) energy entropy and optimized structural parameters least squares support vector machine (LSSVM) is put forward in this paper. Firstly, the original output fault signals are pretreatment with EEMD, and then the EEMD energy entropy is extracted as the fault feature vector. Then the radial basis functio...
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ژورنال
عنوان ژورنال: Shock and Vibration
سال: 2017
ISSN: 1070-9622,1875-9203
DOI: 10.1155/2017/2623081