The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions f...The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition assessments.In this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring tasks.The primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as buildings.The algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone swarm.First,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the swarm.Second,the algorithm identifies observers within the swarm based on the current orientation.To further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy logic.This approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular velocity.The integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle avoidance.The proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive simulations.The results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.展开更多
The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sens...The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.展开更多
A technique of detecting cutting tool fracture and ultimate wear by si- multaneously monitoring both the spindle motor current and cutting process related acoustic emission(AE)in the cutting process is reported.The te...A technique of detecting cutting tool fracture and ultimate wear by si- multaneously monitoring both the spindle motor current and cutting process related acoustic emission(AE)in the cutting process is reported.The technique can detect breakage of drills having diameter over 0.8mm,turning cutter crack of area over 0.2mm,and the ultimate wear.The principle,system construction,experimental method and result of the technique are discussed.The ratio of success in detection approaches 96% or higher.展开更多
Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted ...Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.展开更多
Triboelectric nanogenerators(TENGs)have potential to achieve energy harvesting and condition monitoring of oils,the“lifeblood”of industry.However,oil absorption on the solid surfaces is a great challenge for oil-sol...Triboelectric nanogenerators(TENGs)have potential to achieve energy harvesting and condition monitoring of oils,the“lifeblood”of industry.However,oil absorption on the solid surfaces is a great challenge for oil-solid TENG(O-TENG).Here,oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties.The designed O-TENG can generate an excellent electricity(with a charge density of 9.1μC m^(−2) and a power density of 1.23 mW m^(−2)),which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide.It also has a significant durability(30,000 cycles)and can power a digital thermometer for self-powered sensor applications.Further,a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils.The O-TENG can detect particle contaminants at least down to 0.01 wt%and water contaminants down to 100 ppm,which are much better than previous online monitoring methods(particle>0.1 wt%;water>1000 ppm).More interesting,the developed O-TENG can also distinguish water from other contaminants,which means the developed O-TENG has a highly water-selective performance.This work provides an ideal strategy for enhancing the output and durability of TENGs for oil-solid contact and opens new intelligent pathways for oil-solid energy harvesting and oil condition monitoring.展开更多
On-line measurement for dielectric loss angle can effectively monitor the insulation condition of capacitive equipment in power systems. Synthetic relative measuring methods not only markedly overcome the shortcomings...On-line measurement for dielectric loss angle can effectively monitor the insulation condition of capacitive equipment in power systems. Synthetic relative measuring methods not only markedly overcome the shortcomings of traditional absolute measuring methods but also greatly improve the accuracy of dielectric loss angle measurement. However, synthetic relative measuring methods based on two or three pieces of capacitive equipment do not have the characteristic of generality. In this paper, a principle of synthetic relative measuring method is presented. The example of application for synthetic relative methods based on three and four pieces of capacitive equipment running in the same phase is taken to present the failure judgment matrices for N pieces of equipment. According to these matrices, the fault condition of N pieces of capacitive equipment can be watched, which is more general. Then some problems needing to be concerned along with two diagnostic methods used in diagnostic system are introduced. Finally, two programmable flow charts for the two methods are given and corresponding examples demonstrate their feasibility in practice.展开更多
Real-time acquisition of human pulse signals in daily life is clinically important for cardiovascular disease monitoring and diagnosis.Here,we propose a smart photonic wristband for pulse signal monitoring based on sp...Real-time acquisition of human pulse signals in daily life is clinically important for cardiovascular disease monitoring and diagnosis.Here,we propose a smart photonic wristband for pulse signal monitoring based on speckle pattern analysis with a polymer optical fiber(POF)integrated into a sports wristband.Several different speckle pattern processing algorithms and POFs with different core diameters were evaluated.The results indicated that the smart photonic wristband had a high signal-to-noise ratio and low latency,with the measurement error controlled at approximately 3.7%.This optimized pulse signal could be used for further medical diagnosis and was capable of objectively monitoring subtle pulse signal changes,such as the pulse waveform at different positions of Cunkou and pulse waveforms before and after exercise.With the assistance of artificial intelligence(AI),functions such as gesture recognition have been realized through the established prediction model by processing pulse signals,in which the recognition accuracy reaches 95%.Our AI-assisted smart photonic wristband has potential applications for clinical treatment of cardiovascular diseases and home monitoring,paving the way for medical Internet of Things-enabled smart systems.展开更多
Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e....Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.展开更多
Water-lubrication bearings are critical components in ship operation.However,studies on their maintenance and failure detection are highly limited.The use of sensors to continually monitor the working operation of bea...Water-lubrication bearings are critical components in ship operation.However,studies on their maintenance and failure detection are highly limited.The use of sensors to continually monitor the working operation of bearings is a potential approach to solve this problem,which is collectively called intelligent bearings.In this literature review,the recent progress of electrical resistance strain gauges,Fiber Bragg grating,triboelectric nanogenerators,piezoelectric nanogenerators,and thermoelectric sensors for in-situ monitoring is summarized.Future research and design concepts on intelligent water-lubrication bearings are also comprehensively discussed.The findings show that the accident risks,lubrication condition,and remaining life of water-lubricated bearings can be evaluated with the surface temperature,coefficient of friction,and wear volume monitoring.The research work on intelligent water-lubricated bearings is committed to promoting the development of green,electrified,and intelligent technologies for ship propulsion systems,which have important theoretical significance and application value.展开更多
To protect mining areas from electrical fire, it is very important to install electrical nre momtormg system to ensure safety in development of mineral resources and for buildings. In this paper, design for electrical...To protect mining areas from electrical fire, it is very important to install electrical nre momtormg system to ensure safety in development of mineral resources and for buildings. In this paper, design for electrical fire monitoring and detection system with optional sensor modules has been proposed. In addition, necessity and suitability of electrical fire monitoring and detection system with optional sensor modules in mining areas have been reviewed. The designed electrical fire monitoring and detection system suit- able for work environment of mining industry is composed by host-computer monitoring software and slave-computer detectors. Monitoring detectors are manufactured by using embedded technology. Exter- nal shells deployed have superior enclosure performances and explosion-proof properties. It is easy to install and maintain the system. In general, the system has reached, or even exceeded standards specified in national standards for performances and appearances of such devices. Test results show application of electrical fire monitoring and detection system can effectively enhance monitoring intensity over the mining areas and provide reliable guarantee to ensure orderly development of mineral resources and to protect physical and property safety of citizens in these areas.展开更多
文摘The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition assessments.In this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring tasks.The primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as buildings.The algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone swarm.First,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the swarm.Second,the algorithm identifies observers within the swarm based on the current orientation.To further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy logic.This approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular velocity.The integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle avoidance.The proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive simulations.The results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.
文摘The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.
文摘A technique of detecting cutting tool fracture and ultimate wear by si- multaneously monitoring both the spindle motor current and cutting process related acoustic emission(AE)in the cutting process is reported.The technique can detect breakage of drills having diameter over 0.8mm,turning cutter crack of area over 0.2mm,and the ultimate wear.The principle,system construction,experimental method and result of the technique are discussed.The ratio of success in detection approaches 96% or higher.
基金the National Natural Science Foundations of China(Nos.91860125,51705398)the National Key Basic Research Program of China(No.2015CB057400)the Shaanxi Province 2020 Natural Science Basic Research Plan(No.2020JQ-042).
文摘Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.
基金want to thank Swedish Kempe Scholarship Project(No.JCK-1903.1)the Swedish Research Council for Environment,Agricultural Sciences and Spatial Planning(Formas,No.2019-00904)+1 种基金the Swedish Research Council(No.2019-04941)and the National Natural Science Foundation of China(Grant No.51905027).
文摘Triboelectric nanogenerators(TENGs)have potential to achieve energy harvesting and condition monitoring of oils,the“lifeblood”of industry.However,oil absorption on the solid surfaces is a great challenge for oil-solid TENG(O-TENG).Here,oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties.The designed O-TENG can generate an excellent electricity(with a charge density of 9.1μC m^(−2) and a power density of 1.23 mW m^(−2)),which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide.It also has a significant durability(30,000 cycles)and can power a digital thermometer for self-powered sensor applications.Further,a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils.The O-TENG can detect particle contaminants at least down to 0.01 wt%and water contaminants down to 100 ppm,which are much better than previous online monitoring methods(particle>0.1 wt%;water>1000 ppm).More interesting,the developed O-TENG can also distinguish water from other contaminants,which means the developed O-TENG has a highly water-selective performance.This work provides an ideal strategy for enhancing the output and durability of TENGs for oil-solid contact and opens new intelligent pathways for oil-solid energy harvesting and oil condition monitoring.
文摘On-line measurement for dielectric loss angle can effectively monitor the insulation condition of capacitive equipment in power systems. Synthetic relative measuring methods not only markedly overcome the shortcomings of traditional absolute measuring methods but also greatly improve the accuracy of dielectric loss angle measurement. However, synthetic relative measuring methods based on two or three pieces of capacitive equipment do not have the characteristic of generality. In this paper, a principle of synthetic relative measuring method is presented. The example of application for synthetic relative methods based on three and four pieces of capacitive equipment running in the same phase is taken to present the failure judgment matrices for N pieces of equipment. According to these matrices, the fault condition of N pieces of capacitive equipment can be watched, which is more general. Then some problems needing to be concerned along with two diagnostic methods used in diagnostic system are introduced. Finally, two programmable flow charts for the two methods are given and corresponding examples demonstrate their feasibility in practice.
基金financial supports from National Key R&D Program of China (2022YFE0140400)National Natural Science Foundation of China(62003046, 62111530238)+7 种基金Guangdong Basic and Applied Basic Research Foundation (2021A1515011997)The Supplemental Funds for Major Scientific Research Projects of Beijing Normal University,Zhuhai(ZHPT2023007)Special project in key field of Guangdong Provincial Department of Education (2021ZDZX1050)The Innovation Team Project of Guangdong Provincial Department of Education (2021KCXTD014)Fundação para a Ciência e a Tecnologia (FCT) through the 2021.00667CEECIND (iAqua project)PTDC/EEI-EEE/0415/2021 (DigiAqua project)The project i3N,UIDB/50025/2020 n&UIDP/50025/2020, financed by national funds through the FCT/MEC
文摘Real-time acquisition of human pulse signals in daily life is clinically important for cardiovascular disease monitoring and diagnosis.Here,we propose a smart photonic wristband for pulse signal monitoring based on speckle pattern analysis with a polymer optical fiber(POF)integrated into a sports wristband.Several different speckle pattern processing algorithms and POFs with different core diameters were evaluated.The results indicated that the smart photonic wristband had a high signal-to-noise ratio and low latency,with the measurement error controlled at approximately 3.7%.This optimized pulse signal could be used for further medical diagnosis and was capable of objectively monitoring subtle pulse signal changes,such as the pulse waveform at different positions of Cunkou and pulse waveforms before and after exercise.With the assistance of artificial intelligence(AI),functions such as gesture recognition have been realized through the established prediction model by processing pulse signals,in which the recognition accuracy reaches 95%.Our AI-assisted smart photonic wristband has potential applications for clinical treatment of cardiovascular diseases and home monitoring,paving the way for medical Internet of Things-enabled smart systems.
基金The authors appreciate the financial support provided by the Natural Science Foundation of China(No.41807294)This study was also financially supported by China Geological Survey Project(Nos.DD20190716 and 0001212020CC60002)。
文摘Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.
基金Supported by the National Natural Science Foundation of China(Grant No.52171319).
文摘Water-lubrication bearings are critical components in ship operation.However,studies on their maintenance and failure detection are highly limited.The use of sensors to continually monitor the working operation of bearings is a potential approach to solve this problem,which is collectively called intelligent bearings.In this literature review,the recent progress of electrical resistance strain gauges,Fiber Bragg grating,triboelectric nanogenerators,piezoelectric nanogenerators,and thermoelectric sensors for in-situ monitoring is summarized.Future research and design concepts on intelligent water-lubrication bearings are also comprehensively discussed.The findings show that the accident risks,lubrication condition,and remaining life of water-lubricated bearings can be evaluated with the surface temperature,coefficient of friction,and wear volume monitoring.The research work on intelligent water-lubricated bearings is committed to promoting the development of green,electrified,and intelligent technologies for ship propulsion systems,which have important theoretical significance and application value.
基金the Science & Technology Research and Development Project of Langfang Municipal City for the Year 2013 (No.2013011048)Baoding GEEHO Electric Technology Development Co.,Ltd.for financial support and help in data acquisition and statistics during preparation of this paper
文摘To protect mining areas from electrical fire, it is very important to install electrical nre momtormg system to ensure safety in development of mineral resources and for buildings. In this paper, design for electrical fire monitoring and detection system with optional sensor modules has been proposed. In addition, necessity and suitability of electrical fire monitoring and detection system with optional sensor modules in mining areas have been reviewed. The designed electrical fire monitoring and detection system suit- able for work environment of mining industry is composed by host-computer monitoring software and slave-computer detectors. Monitoring detectors are manufactured by using embedded technology. Exter- nal shells deployed have superior enclosure performances and explosion-proof properties. It is easy to install and maintain the system. In general, the system has reached, or even exceeded standards specified in national standards for performances and appearances of such devices. Test results show application of electrical fire monitoring and detection system can effectively enhance monitoring intensity over the mining areas and provide reliable guarantee to ensure orderly development of mineral resources and to protect physical and property safety of citizens in these areas.
