When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To o...When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.展开更多
Crash prediction models are commonly used for network screening in highway safety management process,where potential impacts of highway safety treatments are quantified.The Highway Safety Manual(HSM)provides crash pre...Crash prediction models are commonly used for network screening in highway safety management process,where potential impacts of highway safety treatments are quantified.The Highway Safety Manual(HSM)provides crash prediction models for various types of highway facilities that are often referred to as safety performance functions(SPFs).Freeway facility SPFs in the HSM were developed using data gathered from the states of California,Maine,and Washington.When applying these HSM-default SPFs to a local jurisdiction,the HSM recommends calibration of HSM-default SPFs or development of jurisdiction-specific SPFs to improve the accuracy of crash predictions.This study first calibrated the HSM-default freeway SPFs and for further accuracy and comparison purposes calibration functions were developed using Kansas freeway data.The performance of calibrated HSM-default SPFs was then compared with developed calibration functions concerning the accuracy in crash prediction.Freeway facility calibration dataset included521 freeway segments,351 entrance-related speed-change lanes,and 366 exit-related speed-change lanes.Cumulative residual plots and several other goodness-of-fit measures were used to assess the quality of calibrated HSM-default SPFs and calibration functions.Calibration functions fitted better compared to calibrated HSM-default SPFs for Kansas freeway data.The methodology used in this study could be beneficial and practiced to any jurisdiction.Calibration functions could be used as an alternative to jurisdiction-specific SPFs or a replacement for HSM-default SPFs,which are frequently used in comparing alternatives,in calculating economic benefits of project improvements,and in estimating economic effectiveness of crash reduction in highway safety-related decision making.展开更多
基金supported by National Natural Science Foundation of China (Grant No. 71271078)National Hi-tech Research and Development Program of China (863 Program, Grant No. 2009AA04Z414)Integration of Industry, Education and Research of Guangdong Province, and Ministry of Education of China (Grant No. 2009B090300312)
文摘When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.
基金supported by the Kansas Department of Transportation(KDOT)under the K-TRAN program.
文摘Crash prediction models are commonly used for network screening in highway safety management process,where potential impacts of highway safety treatments are quantified.The Highway Safety Manual(HSM)provides crash prediction models for various types of highway facilities that are often referred to as safety performance functions(SPFs).Freeway facility SPFs in the HSM were developed using data gathered from the states of California,Maine,and Washington.When applying these HSM-default SPFs to a local jurisdiction,the HSM recommends calibration of HSM-default SPFs or development of jurisdiction-specific SPFs to improve the accuracy of crash predictions.This study first calibrated the HSM-default freeway SPFs and for further accuracy and comparison purposes calibration functions were developed using Kansas freeway data.The performance of calibrated HSM-default SPFs was then compared with developed calibration functions concerning the accuracy in crash prediction.Freeway facility calibration dataset included521 freeway segments,351 entrance-related speed-change lanes,and 366 exit-related speed-change lanes.Cumulative residual plots and several other goodness-of-fit measures were used to assess the quality of calibrated HSM-default SPFs and calibration functions.Calibration functions fitted better compared to calibrated HSM-default SPFs for Kansas freeway data.The methodology used in this study could be beneficial and practiced to any jurisdiction.Calibration functions could be used as an alternative to jurisdiction-specific SPFs or a replacement for HSM-default SPFs,which are frequently used in comparing alternatives,in calculating economic benefits of project improvements,and in estimating economic effectiveness of crash reduction in highway safety-related decision making.
