Induction motors （IMs） are commonly used in various industrial applications. To improve energy con- sumption efficiency, a reliable IM health condition moni- toring system is very useful to detect IM fault at its ea...Induction motors （IMs） are commonly used in various industrial applications. To improve energy con- sumption efficiency, a reliable IM health condition moni- toring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is pro- posed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are syn- thesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air- gap eccentricity diagnosis. The effectiveness of the pro- posed harmonic synthesis technique is examined experi- mentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.展开更多
Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical mod...Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.展开更多
Continuous monitoring of wind turbine(WT)opera-tion can improve the reliability of the wind turbine and lower the operation and maintenance costs.To improve the condition mon-itoring(CM)and fault detection performance...Continuous monitoring of wind turbine(WT)opera-tion can improve the reliability of the wind turbine and lower the operation and maintenance costs.To improve the condition mon-itoring(CM)and fault detection performance on WTs,this paper proposes an artificial intelligence-based probabilistic anomaly detection approach that can not only provide a deterministic estimation of the WT condition but also evaluate the uncertainties associated with the estimation.An abnormal WT condition is detected based on the evaluated uncertainties,to provide a noise-free incipient fault indication.Compared to the conventional deterministic CM approaches with a residual-based anomaly detection criterion,the proposed probabilistic approach tends to accurately detect the faults earlier,which allows more time for maintenance scheduling to prevent WT component failure.The early fault detection ability of the proposed approach was verified on an operational WT in China.展开更多
基金Supported in part by Natural Sciences and Engineering Research Council of Canada(NSERC)eMech Systems IncBare Point Water Treatment Plant in Thunder Bay,Ontario,Canada
文摘Induction motors （IMs） are commonly used in various industrial applications. To improve energy con- sumption efficiency, a reliable IM health condition moni- toring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is pro- posed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are syn- thesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air- gap eccentricity diagnosis. The effectiveness of the pro- posed harmonic synthesis technique is examined experi- mentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.
文摘Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.
基金The work was supported in part by the Australian Research Council(ARC)Discovery Grant(DP170103427/180103217)in part by the Funda-mental Research Funds for the Central Universities(No.2017BSCXB58)and the Postgraduate Research&Practice Innovation Program of Jiangsu Province.
文摘Continuous monitoring of wind turbine(WT)opera-tion can improve the reliability of the wind turbine and lower the operation and maintenance costs.To improve the condition mon-itoring(CM)and fault detection performance on WTs,this paper proposes an artificial intelligence-based probabilistic anomaly detection approach that can not only provide a deterministic estimation of the WT condition but also evaluate the uncertainties associated with the estimation.An abnormal WT condition is detected based on the evaluated uncertainties,to provide a noise-free incipient fault indication.Compared to the conventional deterministic CM approaches with a residual-based anomaly detection criterion,the proposed probabilistic approach tends to accurately detect the faults earlier,which allows more time for maintenance scheduling to prevent WT component failure.The early fault detection ability of the proposed approach was verified on an operational WT in China.
