Road Traffic monitoring involves the collection of data describing the characteristic of vehicles and their movement through road networks. Such data may be used for one of these purposes such as law enforcement, cong...Road Traffic monitoring involves the collection of data describing the characteristic of vehicles and their movement through road networks. Such data may be used for one of these purposes such as law enforcement, congestion and incident detection and increasing road capacity. Transportation is a requirement for every nation regardless of its economy, political stability, population size and technological development. Movement of goods and people from one place to another is crucial to maintain strong economic and political ties between the various components of any given nation among nations. However, there are different modes of transportation and the most paramount one to human beings is road transportation. Due to increase in the modes of transportation, road users encounter different problems such as road blockage and incidents. Therefore there is need to monitor users incidents and to know the causes. Road traffic monitoring can be done manually or using ICT devices. This paper focuses on how the use of ICT devices can enhance road traffic monitoring. It traces the brief history of transportation;it equally discussed road traffic and safety, tools for monitoring road traffic, Intelligent Transportation Systems (ITS) use for traffic monitoring and their benefits. The result shows that the use of ICT devices in road traffic monitoring should be a Millennium Goal for all developed and developing countries because of its numerous advantages in the reduction of the intensity of traffic and other road incidents.展开更多
This study evaluated the possibility of infrared thermography to measure accurately the temperature of elements of a rotating device, within the scope of condition monitoring. The tested machine was a blower coupled t...This study evaluated the possibility of infrared thermography to measure accurately the temperature of elements of a rotating device, within the scope of condition monitoring. The tested machine was a blower coupled to a 500 kW electric motor, that operated in multiples regimes. The thermograms were acquired by a fixed thermographic camera and were processed and recorded every 15 minutes. Because the normal temperature variations could easily mask a drift caused by a failure, a corrected temperature was computed using autorecursive models. It was shown that an efficient temperature correction should compensate for the variations of the process, and for the ambient temperatures variations, either daily or seasonal. The standard deviation of the corrected temperature was of a few tenth of degree, making possible the detection of a drift of less than one degree and the prediction of potential failure.展开更多
Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In...Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.展开更多
文摘Road Traffic monitoring involves the collection of data describing the characteristic of vehicles and their movement through road networks. Such data may be used for one of these purposes such as law enforcement, congestion and incident detection and increasing road capacity. Transportation is a requirement for every nation regardless of its economy, political stability, population size and technological development. Movement of goods and people from one place to another is crucial to maintain strong economic and political ties between the various components of any given nation among nations. However, there are different modes of transportation and the most paramount one to human beings is road transportation. Due to increase in the modes of transportation, road users encounter different problems such as road blockage and incidents. Therefore there is need to monitor users incidents and to know the causes. Road traffic monitoring can be done manually or using ICT devices. This paper focuses on how the use of ICT devices can enhance road traffic monitoring. It traces the brief history of transportation;it equally discussed road traffic and safety, tools for monitoring road traffic, Intelligent Transportation Systems (ITS) use for traffic monitoring and their benefits. The result shows that the use of ICT devices in road traffic monitoring should be a Millennium Goal for all developed and developing countries because of its numerous advantages in the reduction of the intensity of traffic and other road incidents.
文摘This study evaluated the possibility of infrared thermography to measure accurately the temperature of elements of a rotating device, within the scope of condition monitoring. The tested machine was a blower coupled to a 500 kW electric motor, that operated in multiples regimes. The thermograms were acquired by a fixed thermographic camera and were processed and recorded every 15 minutes. Because the normal temperature variations could easily mask a drift caused by a failure, a corrected temperature was computed using autorecursive models. It was shown that an efficient temperature correction should compensate for the variations of the process, and for the ambient temperatures variations, either daily or seasonal. The standard deviation of the corrected temperature was of a few tenth of degree, making possible the detection of a drift of less than one degree and the prediction of potential failure.
基金supported by the National Science Fund for Distinguished Young Scholars of China(52025056)the China Postdoctoral Science Foundation(2023M732789)+1 种基金the China Postdoctoral Innovative Talents Support Program(BX20230290)the Fundamental Research Funds for the Central Universities(xzy012022062).
文摘Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.
