To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. ...To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. First the physical essence of aliasing that occurs is analyzed; second the interpolation algorithm model is setup based on the Hamming window; then the fast implementation of the algorithm using the Newton iteration method is given. Using the numerical simulation the feasibility of algorithm is validated. Finally, the electrical circuit experiment shows the practicality of the algorithm in the electrical engineering.展开更多
Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in gen...Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.展开更多
Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter.The track-before-detect(TBD)filter is an effective way to increase the signal...Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter.The track-before-detect(TBD)filter is an effective way to increase the signal-to-clutter ratio(SCR),thus improving the detection performance of small targets in sea clutter.To cope with the nonstationary characteristic of sea clutter,an easily-implemented Bayesian TBD filter with adaptive detection threshold is proposed and a new parameter estimation method is devised which is integrated into the detection process.The detection threshold is set according to the parameter estimation result under the framework of information theory.For detection of closely spaced targets,those within the same range cell as the one under test are treated as contribution to sea clutter,and a successive elimination method is adopted to detect them.Simulation results prove the effectiveness of the proposed algorithm in detecting small targets in nonstationary sea clutter,especially closely spaced ones.展开更多
The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the ...The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the time-varying behavior caused by speed fluctuation,the phase function of target component is necessary.However,the frequency components induced by different faults interfere with each other.More importantly,the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation.As such,we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit.First,the envelope signal is obtained by applying Hilbert transform to the raw signal.Second,the characteristic frequency is extracted via chirplet path pursuit,and the other underlying components are calculated by the characteristic coefficient.Then,the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency.Next,the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component.Finally,the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies.The method is validated by simulated and experimental signals.展开更多
基金the National Natural Science Foundation of China (90407007 60372001).
文摘To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. First the physical essence of aliasing that occurs is analyzed; second the interpolation algorithm model is setup based on the Hamming window; then the fast implementation of the algorithm using the Newton iteration method is given. Using the numerical simulation the feasibility of algorithm is validated. Finally, the electrical circuit experiment shows the practicality of the algorithm in the electrical engineering.
基金Project(U1709211) supported by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization,ChinaProject(ICT2021A15) supported by the State Key Laboratory of Industrial Control Technology,Zhejiang University,ChinaProject(TPL2019C03) supported by Open Fund of Science and Technology on Thermal Energy and Power Laboratory,China。
文摘Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.
基金supported by the National Natural Science Foundation of China(61671139)。
文摘Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter.The track-before-detect(TBD)filter is an effective way to increase the signal-to-clutter ratio(SCR),thus improving the detection performance of small targets in sea clutter.To cope with the nonstationary characteristic of sea clutter,an easily-implemented Bayesian TBD filter with adaptive detection threshold is proposed and a new parameter estimation method is devised which is integrated into the detection process.The detection threshold is set according to the parameter estimation result under the framework of information theory.For detection of closely spaced targets,those within the same range cell as the one under test are treated as contribution to sea clutter,and a successive elimination method is adopted to detect them.Simulation results prove the effectiveness of the proposed algorithm in detecting small targets in nonstationary sea clutter,especially closely spaced ones.
基金Project(2018YJS137)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(51275030)supported by the National Natural Science Foundation of China
文摘The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the time-varying behavior caused by speed fluctuation,the phase function of target component is necessary.However,the frequency components induced by different faults interfere with each other.More importantly,the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation.As such,we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit.First,the envelope signal is obtained by applying Hilbert transform to the raw signal.Second,the characteristic frequency is extracted via chirplet path pursuit,and the other underlying components are calculated by the characteristic coefficient.Then,the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency.Next,the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component.Finally,the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies.The method is validated by simulated and experimental signals.
