Almost all work on model-based diagnosis （MBD） potentially presumes faults are per- sistent and does not take intermittent faults （IFs） into account. Therefore, it is common for diag- nosis systems to misjudge IFs...Almost all work on model-based diagnosis （MBD） potentially presumes faults are per- sistent and does not take intermittent faults （IFs） into account. Therefore, it is common for diag- nosis systems to misjudge IFs as permanent faults （PFs）, which are the major cause of the problems of false alarms, cannot duplication and no fault found in aircraft avionics. To address this problem, a new fault model which includes PFs and IFs is presented based on discrete event systems （DESs）. Thereafter, an approach is given to discriminate between PFs and IFs by diagnosing the current fault. In this paper, the regulations of （PFs and IFs） fault evolution through fault and reset events along the traces of system are studied, and then label propagation function is modified to account for PFs and the dynamic behavior of IFs and diagnosability of PFs and IFs are defined. Finally, illustrative examples are presented to demonstrate the proposed approach, and the analysis results show the fault types can be discriminated within bounded delay if the system is diagnosable.展开更多
Background:The theory of pulse diagnosis is to assess the physiological condition of the human body using radial pulse.However,pulses can vary markedly from person to person.Further,pulse diagnosis is difficult to lea...Background:The theory of pulse diagnosis is to assess the physiological condition of the human body using radial pulse.However,pulses can vary markedly from person to person.Further,pulse diagnosis is difficult to learn and requires one-on-one teaching.Methods:To address this problem,we built a home-made pulse diagnoser and measured human pulses for studying the standardization of pulse diagnosis.Our pulse diagnoser was composed of a piezoelectric transducer,differential amplifier,data acquisition instrument,and a Matlab analysis program.The measured pulses were converted into electronic signals by a piezoelectric transducer and saved on a computer.The digitalized data were then refined and analyzed by fast Fourier transform for frequency analysis.Simulations were performed to assess the factors that affected the pulse,including phase shifts of reflected pulse waves (generate sub-peaks in pulses),inconsistent heart rates (deform pulse waves),the vessel stiffness (alter harmonic frequencies of the pulses),and the wave amplitudes (determined by the pulse strength).Results:By comparing a published report and our simulation findings,we characterized the pulse types and the effects of various factors,and then applied the findings to study actual pulses in patients.Three types of pulses were determined from the frequency spectrumchoppy pulse (Se mai) without apparent harmonic peaks,the harmonic frequencies of wiry pulse (Xian mai) that are non-integer multiples of the fundamental frequency,and surging pulse (Hong mai) that exhibit strong amplitudes in the spectrum of frequency.A normal pulse and a slippery pulse were differentiated by a phase shift,but not by assessing the frequency spectrum.Conclusion:These findings confirm that frequency domain analysis can avoid ambiguity arising in assessing the three types of pulses in the time domain.Further studies of other pulses in the frequency domain are required to develop a precise electronic pulse diagnoser.展开更多
逻辑自动机下的可预测性分析趋于保守,通常在实际系统中应用受限。该文研究基于随机自动机的故障预测问题。对于每一个正常的系统状态,应用吸收概率理论计算其转移到故障状态的概率和平均时间。根据系统事件的可观测性构造诊断器,确定...逻辑自动机下的可预测性分析趋于保守,通常在实际系统中应用受限。该文研究基于随机自动机的故障预测问题。对于每一个正常的系统状态,应用吸收概率理论计算其转移到故障状态的概率和平均时间。根据系统事件的可观测性构造诊断器,确定系统可能处于的状态集合。基于观测序列,确定系统状态分布,通过概率加权计算系统转移到故障状态的概率和平均时间。应用HVAC(heating,ventilation and air conditioning)系统的仿真实例验证算法的有效性。结果表明:该方法能够预测不同观测序列下系统发生故障的概率和平均时间。此外,对于逻辑不可预测系统,该方法依然适用。展开更多
基金co-supported by National Natural Science Foundation of China (No. 51175502)National Defence Pre-research Foundation of China (No. 9140A17060411KG01)
文摘Almost all work on model-based diagnosis （MBD） potentially presumes faults are per- sistent and does not take intermittent faults （IFs） into account. Therefore, it is common for diag- nosis systems to misjudge IFs as permanent faults （PFs）, which are the major cause of the problems of false alarms, cannot duplication and no fault found in aircraft avionics. To address this problem, a new fault model which includes PFs and IFs is presented based on discrete event systems （DESs）. Thereafter, an approach is given to discriminate between PFs and IFs by diagnosing the current fault. In this paper, the regulations of （PFs and IFs） fault evolution through fault and reset events along the traces of system are studied, and then label propagation function is modified to account for PFs and the dynamic behavior of IFs and diagnosability of PFs and IFs are defined. Finally, illustrative examples are presented to demonstrate the proposed approach, and the analysis results show the fault types can be discriminated within bounded delay if the system is diagnosable.
基金the National Natural Science Foundation of China(81473597)China National Funds for Distinguished Young Scientists(30825046)Chang Jiang Scholars Program,and the 111 Project(B07007).
文摘Background:The theory of pulse diagnosis is to assess the physiological condition of the human body using radial pulse.However,pulses can vary markedly from person to person.Further,pulse diagnosis is difficult to learn and requires one-on-one teaching.Methods:To address this problem,we built a home-made pulse diagnoser and measured human pulses for studying the standardization of pulse diagnosis.Our pulse diagnoser was composed of a piezoelectric transducer,differential amplifier,data acquisition instrument,and a Matlab analysis program.The measured pulses were converted into electronic signals by a piezoelectric transducer and saved on a computer.The digitalized data were then refined and analyzed by fast Fourier transform for frequency analysis.Simulations were performed to assess the factors that affected the pulse,including phase shifts of reflected pulse waves (generate sub-peaks in pulses),inconsistent heart rates (deform pulse waves),the vessel stiffness (alter harmonic frequencies of the pulses),and the wave amplitudes (determined by the pulse strength).Results:By comparing a published report and our simulation findings,we characterized the pulse types and the effects of various factors,and then applied the findings to study actual pulses in patients.Three types of pulses were determined from the frequency spectrumchoppy pulse (Se mai) without apparent harmonic peaks,the harmonic frequencies of wiry pulse (Xian mai) that are non-integer multiples of the fundamental frequency,and surging pulse (Hong mai) that exhibit strong amplitudes in the spectrum of frequency.A normal pulse and a slippery pulse were differentiated by a phase shift,but not by assessing the frequency spectrum.Conclusion:These findings confirm that frequency domain analysis can avoid ambiguity arising in assessing the three types of pulses in the time domain.Further studies of other pulses in the frequency domain are required to develop a precise electronic pulse diagnoser.
文摘逻辑自动机下的可预测性分析趋于保守,通常在实际系统中应用受限。该文研究基于随机自动机的故障预测问题。对于每一个正常的系统状态,应用吸收概率理论计算其转移到故障状态的概率和平均时间。根据系统事件的可观测性构造诊断器,确定系统可能处于的状态集合。基于观测序列,确定系统状态分布,通过概率加权计算系统转移到故障状态的概率和平均时间。应用HVAC(heating,ventilation and air conditioning)系统的仿真实例验证算法的有效性。结果表明:该方法能够预测不同观测序列下系统发生故障的概率和平均时间。此外,对于逻辑不可预测系统,该方法依然适用。
