As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system sc...As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.展开更多
The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the s...The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.展开更多
The mission reliability assessment plays a great role in logistics planning and supporting resource optimization of complex system.But the current problem,which is difficult to solve,is how to model and analyze the ch...The mission reliability assessment plays a great role in logistics planning and supporting resource optimization of complex system.But the current problem,which is difficult to solve,is how to model and analyze the characters of system reliability under the complex mission profile.In order to solve the problem,an agentbased simulation method was used to assess reliability for complex systems with various random working conditions.A multi-working condition simulation agent(MA)was designed and used to simulate the random transferring process of working conditions of system,and it cooperated with system simulation agents(SAs)and unit simulation agents(UAs)to realize system mission reliability(MR)simulation.Through simulation experiments,effect of multiple working conditions mission on the reliability of system was analyzed by comparing with the basic reliability condition.Feasibility and efficiency of the method were proved through simulation experiments of the case system.The research result provides a viable and useful method and a solution for MR analysis and assessment of complex systems in multi-working conditions,which can help to evaluate the reliability of operating system orienting to the practical mission and environment,and it is meaningful for the reliability analysis and the design of complex systems.展开更多
基金supported by the National Natural Sciences Foundation of China(62125302,62203087)Liaoning Revitalization Talents Program(XLYC2002087)+1 种基金Sci-Tech Talent Innovation Support Program of Dalian(2022RG03)Young Elite Scientist Sponsorship Program by China Association for Science and Technology(YESS20220018)
文摘As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.
基金the National Key Research and Development Program of China(No.2020YFB1713500)the Natural Science Basic Research Program of Shaanxi(Grant No.2023JCYB289)+1 种基金the National Natural Science Foundation of China(Grant No.52175112)the Fundamental Research Funds for the Central Universities(Grant No.ZYTS23102).
文摘The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.
文摘The mission reliability assessment plays a great role in logistics planning and supporting resource optimization of complex system.But the current problem,which is difficult to solve,is how to model and analyze the characters of system reliability under the complex mission profile.In order to solve the problem,an agentbased simulation method was used to assess reliability for complex systems with various random working conditions.A multi-working condition simulation agent(MA)was designed and used to simulate the random transferring process of working conditions of system,and it cooperated with system simulation agents(SAs)and unit simulation agents(UAs)to realize system mission reliability(MR)simulation.Through simulation experiments,effect of multiple working conditions mission on the reliability of system was analyzed by comparing with the basic reliability condition.Feasibility and efficiency of the method were proved through simulation experiments of the case system.The research result provides a viable and useful method and a solution for MR analysis and assessment of complex systems in multi-working conditions,which can help to evaluate the reliability of operating system orienting to the practical mission and environment,and it is meaningful for the reliability analysis and the design of complex systems.
