Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF iron...Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.展开更多
The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased si...The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.展开更多
The industrial Internet of Things(IoT)is a trend of factory development and a basic condition of intelligent factory.It is very important to ensure the security of data transmission in industrial IoT.Applying a new ch...The industrial Internet of Things(IoT)is a trend of factory development and a basic condition of intelligent factory.It is very important to ensure the security of data transmission in industrial IoT.Applying a new chaotic secure communication scheme to address the security problem of data transmission is the main contribution of this paper.The scheme is proposed and studied based on the synchronization of different-structure fractional-order chaotic systems with different order.The Lyapunov stability theory is used to prove the synchronization between the fractional-order drive system and the response system.The encryption and decryption process of the main data signals is implemented by using the n-shift encryption principle.We calculate and analyze the key space of the scheme.Numerical simulations are introduced to show the effectiveness of theoretical approach we proposed.展开更多
Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is p...Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.展开更多
Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems.Both structural and non-structural data of industrial systems are collected,which cove...Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems.Both structural and non-structural data of industrial systems are collected,which covers data formats of time-series,text,images,sound,etc.Several researchers discussed above were mostly qualitative,and ceratin techniques need expert guidance to conclude on the condition of gearboxes.But,in this study,an improved symbiotic organism search with deep learning enabled fault diagnosis(ISOSDL-FD)model for gearbox fault detection in industrial systems.The proposed ISOSDL-FD technique majorly concentrates on the identification and classification of faults in the gearbox data.In addition,a Fast kurtogram based time-frequency analysis can be used for revealing the energy present in the machinery signals in the time-frequency representation.Moreover,the deep bidirectional recurrent neural network(DBiRNN)is applied for fault detection and classification.At last,the ISOS approach was derived for optimal hyperparameter tuning of the DL method so that the classification performance will be improvised.To illustrate the improvised performance of the ISOSDL-FD algorithm,a comprehensive experimental analysis can be performed.The experimental results stated the betterment of the ISOSDLFD algorithm over current techniques.展开更多
To evaluate and improve the real-time performance of Ethernet for plant automation(EPA) industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By...To evaluate and improve the real-time performance of Ethernet for plant automation(EPA) industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By analyzing information transmission regularity and EPA deterministic scheduling mechanism,periodic messages were categorized as different modes according to their entering-queue time.The scheduling characteristics and delivery time of each mode and their interacting relations were studied,during which the models of real-time performance of periodic information transmission in EPA system were established.On this basis,an experimental platform is developed to test the delivery time of periodic messages transmission in EPA system.According to the analysis and the experiment,the main factors that limit the real-time performance of EPA periodic data transmission and the improvement methods were proposed.展开更多
Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the re...Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.展开更多
With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests...With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.展开更多
As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more challenges.Currently,the data aggregation protoco...As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more challenges.Currently,the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating,alternately,the aim at enhancing the security of data.The performances of the secure data aggregation protocols are the trade-off of several metrics,which involves the transmission/fusion,the energy efficiency and the security in Wireless Sensor Network(WSN).Unfortunately,there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN.In consideration of IWSN,we firstly review the security requirements and techniques in WSN data aggregation in this paper.Then,we give a holistic overview of the classical secure data aggregation protocols,which are divided into three categories:hop-by-hop encrypted data aggregation,end-to-end encrypted data aggregation and unencrypted secure data aggregation.Along this way,combining with the characteristics of industrial applications,we analyze the pros and cons of the existing security schemes in each category qualitatively,and realize that the security and the energy efficiency are suitable for IWSN.Finally,we make the conclusion about the techniques and approach in these categories,and highlight the future research directions of privacy preserving data aggregation in IWSN.展开更多
In recent years, China's economic growth speed has been slowing down, leading to the problems of overcapacity and unbalanced regional economic development, and the mismatch between industrial and financial structu...In recent years, China's economic growth speed has been slowing down, leading to the problems of overcapacity and unbalanced regional economic development, and the mismatch between industrial and financial structure is becoming intense. Therefore, this paper, starting with the relationship among economic growth, industrial structure and financial structure, summarizes the research by the former scholars. On this basis, by using data of 31 provincial panel data in China from 2007 to 2016, the article aims to find out the relationship between the industrial structure and economic growth, the relationship between the financial structure and economic growth and the relationship between the interaction of financial and industrial structure and economic growth. Finally, the corresponding policy recommendations are obtained following the systematical empirical conclusions. The conclusions of this paper are as follows:(1) developing indirect financing mode can effectively drive China's economic growth.(2) continuing to develop the second industry can play a catalytic role in the economic growth in most areas of China.(3) the interaction between the financial structure and the industrial structure can promote the economic growth significantly. However, the matching effect of the financial structure and industrial structure in China has not been completely formed, and the industrial upgrading should be guided to be structurally reformed through the policy.展开更多
There is a growing demand for time series data analysis in industry areas.Apache loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O performance.With User-Defined Func...There is a growing demand for time series data analysis in industry areas.Apache loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O performance.With User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB directly.To satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache loTDB.This library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,etc.loTDQ enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability analysis.It provides a framework for users to examine loT time series with data quality problems.Experiments show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.展开更多
Although a number of studies have been published in the general area on various factors affecting the ecologicalization of urban industrial structure,little work has been carried out for empirical studies quantitative...Although a number of studies have been published in the general area on various factors affecting the ecologicalization of urban industrial structure,little work has been carried out for empirical studies quantitatively analyzing the relevance between green finance development and the ecologicalization of urban industrial structure.Therefore,based on a comprehensive index of green finance development,this research employs panel data of target cities1 for the period 2012–2020 to explore the influence of green finance on the ecologicalization of urban industrial structure.The empirical results show that green finance development significantly improves the ecologicalization level of urban industrial structure.In addition,it is found that green finance plays a stronger role in promoting the ecologicalization of industrial structure in economically developed regions than in economically underdeveloped regions2.The research results can provide a valuable policy reference for urban green financial market planning and green product innovation.展开更多
Recently developed fault classification methods for industrial processes are mainly data-driven.Notably,models based on deep neural networks have significantly improved fault classification accuracy owing to the inclu...Recently developed fault classification methods for industrial processes are mainly data-driven.Notably,models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data patterns.However,these data-driven models are vulnerable to adversarial attacks;thus,small perturbations on the samples can cause the models to provide incorrect fault predictions.Several recent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarial samples.This paper proposes a black-box attack method with an extreme constraint for a safe-critical industrial fault classification system:Only one variable can be perturbed to craft adversarial samples.Moreover,to hide the adversarial samples in the visualization space,a Jacobian matrix is used to guide the perturbed variable selection,making the adversarial samples in the dimensional reduction space invisible to the human eye.Using the one-variable attack(OVA)method,we explore the vulnerability of industrial variables and fault types,which can help understand the geometric characteristics of fault classification systems.Based on the attack method,a corresponding adversarial training defense method is also proposed,which efficiently defends against an OVA and improves the prediction accuracy of the classifiers.In experiments,the proposed method was tested on two datasets from the Tennessee–Eastman process(TEP)and steel plates(SP).We explore the vulnerability and correlation within variables and faults and verify the effectiveness of OVAs and defenses for various classifiers and datasets.For industrial fault classification systems,the attack success rate of our method is close to(on TEP)or even higher than(on SP)the current most effective first-order white-box attack method,which requires perturbation of all variables.展开更多
The industrial Internet has germinated with the integration of the traditional industry and information technologies.An identifier is the identification of an object in the industrial Internet.The identifier technolog...The industrial Internet has germinated with the integration of the traditional industry and information technologies.An identifier is the identification of an object in the industrial Internet.The identifier technology is a method to validate the identification of an object and trace it.The identifier is a bridge to connect information islands in the industry,as well as the data basis for building a technology application ecosystem based on identifier resolution.We propose three practical applications and application scenarios of the industrial Internet identifier in this paper.Future applications of identifier resolution in the industrial Internet field are also presented.展开更多
This paper pretends to approach and analyse opportunities and risks that arise under the industrial digital paradigm.Known by different names like Industry 4.0,Smart Manufacturing,or Production 4.0,among other terms d...This paper pretends to approach and analyse opportunities and risks that arise under the industrial digital paradigm.Known by different names like Industry 4.0,Smart Manufacturing,or Production 4.0,among other terms digitalization in industry is advancing at a tremendous speed,and is pushing established firms to change and adopt new tools.Besides,it opens opportunities to technological startups to deliver new products and services to the industrial market.As an example of opportunities in operating models,it is clear that digitalization under the model Industry 4.0 and the advantages of Industrial Internet of Things(IIoT),allows faster response to customer demands,increases flexibility allowing the adaptability to manufacturing processes,and provides a tremendous amount of tools for quality improvement in the processes,among other advantages.This article addresses the data driven organization as digitalization evolves and the progress of Artificial Intelligence(AI)and Machine Learning(ML)solutions for industry.展开更多
According to statistics of Printing and Printing Equipment Industries Association of China (PEIAC), the total output value of printing industry of China in 2007 reached 440 billion RMB , the total output value of prin...According to statistics of Printing and Printing Equipment Industries Association of China (PEIAC), the total output value of printing industry of China in 2007 reached 440 billion RMB , the total output value of printing equipment was展开更多
In this paper, we construct a model in which the impact of pollution on health is exerted through both direct and indirect channels. The indirect channel is captured by a production func-tion in which the principal he...In this paper, we construct a model in which the impact of pollution on health is exerted through both direct and indirect channels. The indirect channel is captured by a production func-tion in which the principal health-improving factor, income growth, can be realized only in the cost of pollution increase. This model is then tested by the aggregated chronicle disease data in over 78 Chinese counties. Our results show, after attaining the threshold of 8 μg/m2, continuous increase in industrial SO2 emission density will lead the ratio of population suffering chronicle diseases, among which respiratory diseases occupy a significant proportion, to rise. However, owing to technological progress in pollution control activities, the needed SO2 emission to produce one unit of GDP diminishes with time. Therefore, the negative effect from pollution augmentation on public health seems to be recompensed more and more by the positive effect of economic growth.展开更多
基金financially supported by the General Program of the National Natural Science Foundation of China(No.52274326)the Fundamental Research Funds for the Central Universities (Nos.2125018 and 2225008)China Baowu Low Carbon Metallurgy Innovation Foundation(BWLCF202109)。
文摘Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.
基金supported in part by the National Natural Science Foundation of China(NSFC)(92167106,61833014)Key Research and Development Program of Zhejiang Province(2022C01206)。
文摘The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.
基金supported in part by the National Science Foundation Project of China (61931001, 61873026)the National Key R&D Program of China (2017YFC0820700)
文摘The industrial Internet of Things(IoT)is a trend of factory development and a basic condition of intelligent factory.It is very important to ensure the security of data transmission in industrial IoT.Applying a new chaotic secure communication scheme to address the security problem of data transmission is the main contribution of this paper.The scheme is proposed and studied based on the synchronization of different-structure fractional-order chaotic systems with different order.The Lyapunov stability theory is used to prove the synchronization between the fractional-order drive system and the response system.The encryption and decryption process of the main data signals is implemented by using the n-shift encryption principle.We calculate and analyze the key space of the scheme.Numerical simulations are introduced to show the effectiveness of theoretical approach we proposed.
基金This work was supported in part by the National Natural Science Foundation of China(51435009)Shanghai Sailing Program(19YF1401500)the Fundamental Research Funds for the Central Universities(2232019D3-34).
文摘Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.
文摘Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems.Both structural and non-structural data of industrial systems are collected,which covers data formats of time-series,text,images,sound,etc.Several researchers discussed above were mostly qualitative,and ceratin techniques need expert guidance to conclude on the condition of gearboxes.But,in this study,an improved symbiotic organism search with deep learning enabled fault diagnosis(ISOSDL-FD)model for gearbox fault detection in industrial systems.The proposed ISOSDL-FD technique majorly concentrates on the identification and classification of faults in the gearbox data.In addition,a Fast kurtogram based time-frequency analysis can be used for revealing the energy present in the machinery signals in the time-frequency representation.Moreover,the deep bidirectional recurrent neural network(DBiRNN)is applied for fault detection and classification.At last,the ISOS approach was derived for optimal hyperparameter tuning of the DL method so that the classification performance will be improvised.To illustrate the improvised performance of the ISOSDL-FD algorithm,a comprehensive experimental analysis can be performed.The experimental results stated the betterment of the ISOSDLFD algorithm over current techniques.
基金Supported by the National High Technology Research and Development Program of China (2006AA040301-4,2007AA041301-6)
文摘To evaluate and improve the real-time performance of Ethernet for plant automation(EPA) industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By analyzing information transmission regularity and EPA deterministic scheduling mechanism,periodic messages were categorized as different modes according to their entering-queue time.The scheduling characteristics and delivery time of each mode and their interacting relations were studied,during which the models of real-time performance of periodic information transmission in EPA system were established.On this basis,an experimental platform is developed to test the delivery time of periodic messages transmission in EPA system.According to the analysis and the experiment,the main factors that limit the real-time performance of EPA periodic data transmission and the improvement methods were proposed.
基金supported by the National Natural Science Foundation of China(61773087)the National Key Research and Development Program of China(2018YFB1601500)High-tech Ship Research Project of Ministry of Industry and Information Technology-Research of Intelligent Ship Testing and Verifacation([2018]473)
文摘Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.
基金supported by the National Natural Science Foundation of China (62272078)。
文摘With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.
基金partially supported by the National Natural Science Foundation of China(61571004)the Shanghai Natural Science Foundation(No.17ZR1429100)+1 种基金the National Science and Technology Major Project of China(No.2018ZX03001017-004)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(No.YJKYYQ20170074).
文摘As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more challenges.Currently,the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating,alternately,the aim at enhancing the security of data.The performances of the secure data aggregation protocols are the trade-off of several metrics,which involves the transmission/fusion,the energy efficiency and the security in Wireless Sensor Network(WSN).Unfortunately,there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN.In consideration of IWSN,we firstly review the security requirements and techniques in WSN data aggregation in this paper.Then,we give a holistic overview of the classical secure data aggregation protocols,which are divided into three categories:hop-by-hop encrypted data aggregation,end-to-end encrypted data aggregation and unencrypted secure data aggregation.Along this way,combining with the characteristics of industrial applications,we analyze the pros and cons of the existing security schemes in each category qualitatively,and realize that the security and the energy efficiency are suitable for IWSN.Finally,we make the conclusion about the techniques and approach in these categories,and highlight the future research directions of privacy preserving data aggregation in IWSN.
文摘In recent years, China's economic growth speed has been slowing down, leading to the problems of overcapacity and unbalanced regional economic development, and the mismatch between industrial and financial structure is becoming intense. Therefore, this paper, starting with the relationship among economic growth, industrial structure and financial structure, summarizes the research by the former scholars. On this basis, by using data of 31 provincial panel data in China from 2007 to 2016, the article aims to find out the relationship between the industrial structure and economic growth, the relationship between the financial structure and economic growth and the relationship between the interaction of financial and industrial structure and economic growth. Finally, the corresponding policy recommendations are obtained following the systematical empirical conclusions. The conclusions of this paper are as follows:(1) developing indirect financing mode can effectively drive China's economic growth.(2) continuing to develop the second industry can play a catalytic role in the economic growth in most areas of China.(3) the interaction between the financial structure and the industrial structure can promote the economic growth significantly. However, the matching effect of the financial structure and industrial structure in China has not been completely formed, and the industrial upgrading should be guided to be structurally reformed through the policy.
文摘There is a growing demand for time series data analysis in industry areas.Apache loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O performance.With User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB directly.To satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache loTDB.This library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,etc.loTDQ enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability analysis.It provides a framework for users to examine loT time series with data quality problems.Experiments show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.
基金supported by Shandong Province Key Research and Development Program(Soft Science Project)(No.2021RKY01007).
文摘Although a number of studies have been published in the general area on various factors affecting the ecologicalization of urban industrial structure,little work has been carried out for empirical studies quantitatively analyzing the relevance between green finance development and the ecologicalization of urban industrial structure.Therefore,based on a comprehensive index of green finance development,this research employs panel data of target cities1 for the period 2012–2020 to explore the influence of green finance on the ecologicalization of urban industrial structure.The empirical results show that green finance development significantly improves the ecologicalization level of urban industrial structure.In addition,it is found that green finance plays a stronger role in promoting the ecologicalization of industrial structure in economically developed regions than in economically underdeveloped regions2.The research results can provide a valuable policy reference for urban green financial market planning and green product innovation.
基金This work was supported in part by the National Natural Science Foundation of China(NSFC)(92167106,62103362,and 61833014)the Natural Science Foundation of Zhejiang Province(LR18F030001).
文摘Recently developed fault classification methods for industrial processes are mainly data-driven.Notably,models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data patterns.However,these data-driven models are vulnerable to adversarial attacks;thus,small perturbations on the samples can cause the models to provide incorrect fault predictions.Several recent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarial samples.This paper proposes a black-box attack method with an extreme constraint for a safe-critical industrial fault classification system:Only one variable can be perturbed to craft adversarial samples.Moreover,to hide the adversarial samples in the visualization space,a Jacobian matrix is used to guide the perturbed variable selection,making the adversarial samples in the dimensional reduction space invisible to the human eye.Using the one-variable attack(OVA)method,we explore the vulnerability of industrial variables and fault types,which can help understand the geometric characteristics of fault classification systems.Based on the attack method,a corresponding adversarial training defense method is also proposed,which efficiently defends against an OVA and improves the prediction accuracy of the classifiers.In experiments,the proposed method was tested on two datasets from the Tennessee–Eastman process(TEP)and steel plates(SP).We explore the vulnerability and correlation within variables and faults and verify the effectiveness of OVAs and defenses for various classifiers and datasets.For industrial fault classification systems,the attack success rate of our method is close to(on TEP)or even higher than(on SP)the current most effective first-order white-box attack method,which requires perturbation of all variables.
文摘The industrial Internet has germinated with the integration of the traditional industry and information technologies.An identifier is the identification of an object in the industrial Internet.The identifier technology is a method to validate the identification of an object and trace it.The identifier is a bridge to connect information islands in the industry,as well as the data basis for building a technology application ecosystem based on identifier resolution.We propose three practical applications and application scenarios of the industrial Internet identifier in this paper.Future applications of identifier resolution in the industrial Internet field are also presented.
文摘This paper pretends to approach and analyse opportunities and risks that arise under the industrial digital paradigm.Known by different names like Industry 4.0,Smart Manufacturing,or Production 4.0,among other terms digitalization in industry is advancing at a tremendous speed,and is pushing established firms to change and adopt new tools.Besides,it opens opportunities to technological startups to deliver new products and services to the industrial market.As an example of opportunities in operating models,it is clear that digitalization under the model Industry 4.0 and the advantages of Industrial Internet of Things(IIoT),allows faster response to customer demands,increases flexibility allowing the adaptability to manufacturing processes,and provides a tremendous amount of tools for quality improvement in the processes,among other advantages.This article addresses the data driven organization as digitalization evolves and the progress of Artificial Intelligence(AI)and Machine Learning(ML)solutions for industry.
文摘According to statistics of Printing and Printing Equipment Industries Association of China (PEIAC), the total output value of printing industry of China in 2007 reached 440 billion RMB , the total output value of printing equipment was
文摘In this paper, we construct a model in which the impact of pollution on health is exerted through both direct and indirect channels. The indirect channel is captured by a production func-tion in which the principal health-improving factor, income growth, can be realized only in the cost of pollution increase. This model is then tested by the aggregated chronicle disease data in over 78 Chinese counties. Our results show, after attaining the threshold of 8 μg/m2, continuous increase in industrial SO2 emission density will lead the ratio of population suffering chronicle diseases, among which respiratory diseases occupy a significant proportion, to rise. However, owing to technological progress in pollution control activities, the needed SO2 emission to produce one unit of GDP diminishes with time. Therefore, the negative effect from pollution augmentation on public health seems to be recompensed more and more by the positive effect of economic growth.