Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com-...Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.展开更多
A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensi...A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.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
Prognostics and Health Management(PHM),including monitoring,diagnosis,prognosis,and health management,occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in mo...Prognostics and Health Management(PHM),including monitoring,diagnosis,prognosis,and health management,occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry.With the development of artificial intelligence(AI),especially deep learning(DL)approaches,the application of AI-enabled methods to monitor,diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry.However,there is still a gap to cover monitoring,diagnosis,and prognosis based on AI-enabled methods,simultaneously,and the importance of an open source community,including open source datasets and codes,has not been fully emphasized.To fill this gap,this paper provides a systematic overview of the current development,common technologies,open source datasets,codes,and challenges of AI-enabled PHM methods from three aspects of monitoring,diagnosis,and prognosis.展开更多
Extracting implicit anomaly information through deformation monitoring data mining is highly significant to determining dam safety status.As an intelligent singular value diagnostic method for concrete dam deformation...Extracting implicit anomaly information through deformation monitoring data mining is highly significant to determining dam safety status.As an intelligent singular value diagnostic method for concrete dam deformation monitoring, shallow neural network models result in local optima and overfitting, and require manual feature extraction.To obtain an intelligent singular value diagnosis model that can be used for dam safety monitoring, a convolutional neural network (CNN) model that has advantages of deep learning (DL), such as automatic feature extraction, good model fitting, and strong generalizability, was trained in this study.An engineering example shows that the predicted result of the intelligent singular value diagnostic method based on CNN is highly compatible with the confusion matrix, with a precision of 92.41%, receiver operating characteristic (ROC) coordinates of (0.03, 0.97), an area-under-curve (AUC) value of 0.99, and an F1-score of 0.91.Moreover, the performance of the CNN model is better than those of models based on decision tree (DT) and k-nearest neighbor (KNN) methods.Therefore, the intelligent singular value diagnostic method based on CNN is simple to operate, highly intelligent, and highly reliable, and it has a high potential for application in engineering.展开更多
This paper analyzes the reasons of the tension unbalance of the ropes in multi-rope fric-tion winder, introduces the method of an on-line monitoring rope tensions with a testing device de-veloped by authors, and propo...This paper analyzes the reasons of the tension unbalance of the ropes in multi-rope fric-tion winder, introduces the method of an on-line monitoring rope tensions with a testing device de-veloped by authors, and proposes the criteria of the fault diagnosis and the method of adjustment for the tension unbalance of the ropes, which is important to the theoretical study on the tension unbalance of the ropes and the maintenance of multi-rope winder.展开更多
This paper puts forward the LPM fault diagnosis method in the view of the important purpose of on-line monitoring and fault diagnosis for hoister brake system. The feasibility of the two diagnosis methods are proved i...This paper puts forward the LPM fault diagnosis method in the view of the important purpose of on-line monitoring and fault diagnosis for hoister brake system. The feasibility of the two diagnosis methods are proved in theories; two methods are proved about feasibility and reliability through testing. Two methods are manifestoed that they can undertake the on-line monitoring and fault diagnosis for hoister brake system with satisfied effect.展开更多
Artificial Intelligence(AI)is finding increasing application in healthcare monitoring.Machine learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiolog...Artificial Intelligence(AI)is finding increasing application in healthcare monitoring.Machine learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health data.Thus,early detection of any disease or derangement can aid doctors in saving patients’lives.However,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper classification.We propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health status.To begin with,various patient datasets were collected and trained into the system using IoT sensors.As a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result visibility.Additionally,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant information.Then,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a prescription.We found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.展开更多
Aquaponics are feedback and two player systems, in which fish and crops mutually benefit from one another and, therefore require close monitoring, management and control. Vast amount of data and information flow from ...Aquaponics are feedback and two player systems, in which fish and crops mutually benefit from one another and, therefore require close monitoring, management and control. Vast amount of data and information flow from the aquaponics plant itself with its huge amount of smart sensors for water quality, fish and plant growth, system state etc. and from the stakeholder, e.g., farmers, retailers and end consumers. The intelligent management of aquaponics is only possible if this data and information are managed and used in an intelligent way. Therefore, the main focus of this paper is to introduce an intelligent information management (IIM) for aquaponics. It will be shown how the information can be used to create services such as predictive analytics, system optimization and anomaly detection to improve the aquaponics system. The results show that the system enabled full traceability and transparency in the aquaponics processes (customers can follow what is going on at the farm), reduced water and energy use and increased revenue through early fault detection. In this, paper the information management approach will be introduced and the key benefits of the digitized aquaponics system will be given.展开更多
Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision,hearing,touch,smell,and taste.A gas sensor array(GSA),also known as electronic nose,is a possible solution for ...Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision,hearing,touch,smell,and taste.A gas sensor array(GSA),also known as electronic nose,is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules.Artificial intelligence(AI)applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA.A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring,gas leakage detection,food and beverage production and storage,and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability,low-powerconsumption and ease-of-operation.It is exciting to envisage robots equipped with a"nose"acting as family doctor who will guard every family member's health and keep their home safe.In this review,we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems,exploring their potential applications in disease diagnosis,environmental monitoring,and explosive detection.We also discuss the key limitations of gas sensor units and their possible solutions.Finally,we present the outlook of GSAs over the horizon of smart homes and cities.展开更多
Minerals are the material foundation for advancing human civilization,the starting point of the manufacturing supply chain,and strategic resources essential for national security and economic progress.In recent years,...Minerals are the material foundation for advancing human civilization,the starting point of the manufacturing supply chain,and strategic resources essential for national security and economic progress.In recent years,deep learning and big data have strongly supported improving mining efficiency and safety in underground hard rock mines.Against this backdrop,this paper focuses on the production processes and vital auxiliary aspects of underground mining in hard rock mines.It delves into six aspects:driling,blasting,transportation,hoisting,ventilation,and support and flling.The paper elaborates on the latest advancements in intelligent technology research for each aspect and provides a summary and outlook on the key technologies relevant to these processes.Research results show that the current intelligent technology used in underground mining not only improves production efficiency but also further improves the safety production level of mining enterprises.To achieve intelligent unmanned mining,bottleneck problems in each primary process must be further addressed.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51675098)
文摘Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.
文摘A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.
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.
基金Supported by National Key Research and Development Program of China(Grant No.2018YFB1702400)National Natural Science Foundation of China(Grant Nos.51835009,51705398).
文摘Prognostics and Health Management(PHM),including monitoring,diagnosis,prognosis,and health management,occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry.With the development of artificial intelligence(AI),especially deep learning(DL)approaches,the application of AI-enabled methods to monitor,diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry.However,there is still a gap to cover monitoring,diagnosis,and prognosis based on AI-enabled methods,simultaneously,and the importance of an open source community,including open source datasets and codes,has not been fully emphasized.To fill this gap,this paper provides a systematic overview of the current development,common technologies,open source datasets,codes,and challenges of AI-enabled PHM methods from three aspects of monitoring,diagnosis,and prognosis.
基金supported by the National Natural Science Foundation of China(Grant No.51579207)the Open Foundation of State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area(Grant No.2016ZZKT-8)the Key Projects of Natural Science Basic Research Program of Shaanxi Province(Grant No.2018JZ5010)
文摘Extracting implicit anomaly information through deformation monitoring data mining is highly significant to determining dam safety status.As an intelligent singular value diagnostic method for concrete dam deformation monitoring, shallow neural network models result in local optima and overfitting, and require manual feature extraction.To obtain an intelligent singular value diagnosis model that can be used for dam safety monitoring, a convolutional neural network (CNN) model that has advantages of deep learning (DL), such as automatic feature extraction, good model fitting, and strong generalizability, was trained in this study.An engineering example shows that the predicted result of the intelligent singular value diagnostic method based on CNN is highly compatible with the confusion matrix, with a precision of 92.41%, receiver operating characteristic (ROC) coordinates of (0.03, 0.97), an area-under-curve (AUC) value of 0.99, and an F1-score of 0.91.Moreover, the performance of the CNN model is better than those of models based on decision tree (DT) and k-nearest neighbor (KNN) methods.Therefore, the intelligent singular value diagnostic method based on CNN is simple to operate, highly intelligent, and highly reliable, and it has a high potential for application in engineering.
文摘This paper analyzes the reasons of the tension unbalance of the ropes in multi-rope fric-tion winder, introduces the method of an on-line monitoring rope tensions with a testing device de-veloped by authors, and proposes the criteria of the fault diagnosis and the method of adjustment for the tension unbalance of the ropes, which is important to the theoretical study on the tension unbalance of the ropes and the maintenance of multi-rope winder.
文摘This paper puts forward the LPM fault diagnosis method in the view of the important purpose of on-line monitoring and fault diagnosis for hoister brake system. The feasibility of the two diagnosis methods are proved in theories; two methods are proved about feasibility and reliability through testing. Two methods are manifestoed that they can undertake the on-line monitoring and fault diagnosis for hoister brake system with satisfied effect.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Research Groups under grant number(RGP.1/62/43).
文摘Artificial Intelligence(AI)is finding increasing application in healthcare monitoring.Machine learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health data.Thus,early detection of any disease or derangement can aid doctors in saving patients’lives.However,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper classification.We propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health status.To begin with,various patient datasets were collected and trained into the system using IoT sensors.As a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result visibility.Additionally,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant information.Then,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a prescription.We found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.
文摘Aquaponics are feedback and two player systems, in which fish and crops mutually benefit from one another and, therefore require close monitoring, management and control. Vast amount of data and information flow from the aquaponics plant itself with its huge amount of smart sensors for water quality, fish and plant growth, system state etc. and from the stakeholder, e.g., farmers, retailers and end consumers. The intelligent management of aquaponics is only possible if this data and information are managed and used in an intelligent way. Therefore, the main focus of this paper is to introduce an intelligent information management (IIM) for aquaponics. It will be shown how the information can be used to create services such as predictive analytics, system optimization and anomaly detection to improve the aquaponics system. The results show that the system enabled full traceability and transparency in the aquaponics processes (customers can follow what is going on at the farm), reduced water and energy use and increased revenue through early fault detection. In this, paper the information management approach will be introduced and the key benefits of the digitized aquaponics system will be given.
基金supported by the Hong Kong Innovation and Technology Fund (ITS/115/18) from the Innovation and Technology CommissionShenzhen Science and Technology Innovation Commission (Project No. J CYJ20180306174923335)
文摘Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision,hearing,touch,smell,and taste.A gas sensor array(GSA),also known as electronic nose,is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules.Artificial intelligence(AI)applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA.A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring,gas leakage detection,food and beverage production and storage,and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability,low-powerconsumption and ease-of-operation.It is exciting to envisage robots equipped with a"nose"acting as family doctor who will guard every family member's health and keep their home safe.In this review,we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems,exploring their potential applications in disease diagnosis,environmental monitoring,and explosive detection.We also discuss the key limitations of gas sensor units and their possible solutions.Finally,we present the outlook of GSAs over the horizon of smart homes and cities.
基金supported by the National Natural Science Foundation of China (No.U21A20106)the Chinese Academy of Engineering Project (Nos.2022-33-29 and 2023-XY-44).
文摘Minerals are the material foundation for advancing human civilization,the starting point of the manufacturing supply chain,and strategic resources essential for national security and economic progress.In recent years,deep learning and big data have strongly supported improving mining efficiency and safety in underground hard rock mines.Against this backdrop,this paper focuses on the production processes and vital auxiliary aspects of underground mining in hard rock mines.It delves into six aspects:driling,blasting,transportation,hoisting,ventilation,and support and flling.The paper elaborates on the latest advancements in intelligent technology research for each aspect and provides a summary and outlook on the key technologies relevant to these processes.Research results show that the current intelligent technology used in underground mining not only improves production efficiency but also further improves the safety production level of mining enterprises.To achieve intelligent unmanned mining,bottleneck problems in each primary process must be further addressed.
