Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operati...Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.展开更多
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 by the National Key R&D Program of China(No.2022YFB4301102).
文摘Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.
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.
