A novel robust fault diagnosis scheme, which possesses fault estimate capability as well as fault diagnosis property, is proposed. The scheme is developed based on a suitable combination of the adaptive multiple model...A novel robust fault diagnosis scheme, which possesses fault estimate capability as well as fault diagnosis property, is proposed. The scheme is developed based on a suitable combination of the adaptive multiple model （AMM） and unknown input observer （UIO）. The main idea of the proposed scheme stems from the fact that the actuator Lock-in-Place fault is unknown （when and where the actuator gets locked are unknown）, and multiple models are used to describe different fault scenarios, then a bank of unknown input observers are designed to implement the disturbance de-coupling. According to Lyapunov theory, proof of the robustness of the newly developed scheme in the presence of faults and disturbances is derived. Numerical simulation results on an aircraft example show satisfactory performance of the proposed algorithm.展开更多
Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features,as well as its easy burying in the complex,non-stationary structural vibrations and strong bac...Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features,as well as its easy burying in the complex,non-stationary structural vibrations and strong background noises.In this paper,a method based on the flexible analytical wavelet transform(FAWT)possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings.During the route of the proposed method,the proper FAWT bases are constructed via genetic optimization algorithm(GA)based on maximizing the spectral correlated kurtosis(SCK)which is firstly presented and proved to be efficient and effective in indicating interested fault mode.Via using the customized FAWT bases for each interested fault mode,the original vibration measurements are decomposed into fine frequency subbands,and the sensitive subband which enhances the signal-to-noise ratio(SNR)is selected to exhibit the fault signature on its envelope spectrum.The proposed method is tested via simulated signals,and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace,inner-race and roller defects.The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.展开更多
基金the National Natural Science Foundation of China (60574083)Aeronautics Science Foun-dation of China (2007ZC52039)
文摘A novel robust fault diagnosis scheme, which possesses fault estimate capability as well as fault diagnosis property, is proposed. The scheme is developed based on a suitable combination of the adaptive multiple model （AMM） and unknown input observer （UIO）. The main idea of the proposed scheme stems from the fact that the actuator Lock-in-Place fault is unknown （when and where the actuator gets locked are unknown）, and multiple models are used to describe different fault scenarios, then a bank of unknown input observers are designed to implement the disturbance de-coupling. According to Lyapunov theory, proof of the robustness of the newly developed scheme in the presence of faults and disturbances is derived. Numerical simulation results on an aircraft example show satisfactory performance of the proposed algorithm.
基金co-supported by the Fundamental Research Funds for the Central Universities of China,China Postdoctoral Science Foundation(No.2018M631196)the National Natural Foundation of China(No.51705420).
文摘Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features,as well as its easy burying in the complex,non-stationary structural vibrations and strong background noises.In this paper,a method based on the flexible analytical wavelet transform(FAWT)possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings.During the route of the proposed method,the proper FAWT bases are constructed via genetic optimization algorithm(GA)based on maximizing the spectral correlated kurtosis(SCK)which is firstly presented and proved to be efficient and effective in indicating interested fault mode.Via using the customized FAWT bases for each interested fault mode,the original vibration measurements are decomposed into fine frequency subbands,and the sensitive subband which enhances the signal-to-noise ratio(SNR)is selected to exhibit the fault signature on its envelope spectrum.The proposed method is tested via simulated signals,and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace,inner-race and roller defects.The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.
