Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of...Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.展开更多
Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing processes.In this paper,we briefly discuss the general idea and adva...Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing processes.In this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,etc.Based on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.展开更多
The emerging and development of Artificial Intelligence(AI),especially deep learning,has stimulated its application in various engineering domains.Monitoring,diagnosis and prognosis,as the key elements of intelligence...The emerging and development of Artificial Intelligence(AI),especially deep learning,has stimulated its application in various engineering domains.Monitoring,diagnosis and prognosis,as the key elements of intelligence maintenance of manufacturing systems in the era of Industry 4.0,has also benefited from the advancement of AI technology.The main objective of this special issue aims at bringing scholars to show their research findings in the field of monitoring,diagnosis and prognosis driven by AI,and promote its application in intelligent maintenance of manufacturing system in China.Ten papers have been selected in this special issue after rigorous review and they represent the latest research outcomes in this active area.展开更多
Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpret...Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.展开更多
Rapid advancement over the past decades in nanomanufacturing has led to the realization of a broad range of nanostructures such as nanoparticles,nanotubes,and nanowires.The unique mechanical,chemical,and electrical pr...Rapid advancement over the past decades in nanomanufacturing has led to the realization of a broad range of nanostructures such as nanoparticles,nanotubes,and nanowires.The unique mechanical,chemical,and electrical properties of these nanostructures have made them increasingly desired as key components in industrial and commercial applications.As the geometric dimension of nano-manufactured products is on the sub-micron to nanometer scale,different mechanisms and effects are involved in the nanomanufacturing process as compared to those for macro-scale manufacturing.Although direct measurement methods using atomic force microscopy and electron beam microscopy can determine the dimensions of the nano structure with high accuracy,these methods are not suited for online process control and quality assurance.In comparison,indirect measurement methods analyze in-process parameters as the basis for inferring the dimensional variations in the nano products,thereby enabling online feedback for process control and quality assurance.This paper provides a comprehensive review of relevant indirect measurement methods,starting with their respective working principles,and subsequently discussing their characteristics and applications in terms of two different approaches:data-based and physicsbased methods.Relevant mathematical and physics models for each of the methods are summarized,together with the associated effect of key process parameters on the quality of the final product.Based on the comprehensive literature conducted,it was found that:(1)indirect measurement,especially the data-based method,plays a critical role when it comes to online process control and quality assurance in nanomanufacturing,because of the short processing time compared to the direct method,and(2)physics-based method is providing a way to optimize the process set up for desired geometrical dimensions.展开更多
文摘Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.
基金The work in Section III was supported by the National Science Foundation of China(NSFC)(Nos.52275116,52105112)The work in Section IV was supported by the National Science Foundation of China(NSFC)(Nos.52275117,12127801).
文摘Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing processes.In this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,etc.Based on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.
文摘The emerging and development of Artificial Intelligence(AI),especially deep learning,has stimulated its application in various engineering domains.Monitoring,diagnosis and prognosis,as the key elements of intelligence maintenance of manufacturing systems in the era of Industry 4.0,has also benefited from the advancement of AI technology.The main objective of this special issue aims at bringing scholars to show their research findings in the field of monitoring,diagnosis and prognosis driven by AI,and promote its application in intelligent maintenance of manufacturing system in China.Ten papers have been selected in this special issue after rigorous review and they represent the latest research outcomes in this active area.
基金Supported in part by Science Center for Gas Turbine Project(Project No.P2022-DC-I-003-001)National Natural Science Foundation of China(Grant No.52275130).
文摘Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.
文摘Rapid advancement over the past decades in nanomanufacturing has led to the realization of a broad range of nanostructures such as nanoparticles,nanotubes,and nanowires.The unique mechanical,chemical,and electrical properties of these nanostructures have made them increasingly desired as key components in industrial and commercial applications.As the geometric dimension of nano-manufactured products is on the sub-micron to nanometer scale,different mechanisms and effects are involved in the nanomanufacturing process as compared to those for macro-scale manufacturing.Although direct measurement methods using atomic force microscopy and electron beam microscopy can determine the dimensions of the nano structure with high accuracy,these methods are not suited for online process control and quality assurance.In comparison,indirect measurement methods analyze in-process parameters as the basis for inferring the dimensional variations in the nano products,thereby enabling online feedback for process control and quality assurance.This paper provides a comprehensive review of relevant indirect measurement methods,starting with their respective working principles,and subsequently discussing their characteristics and applications in terms of two different approaches:data-based and physicsbased methods.Relevant mathematical and physics models for each of the methods are summarized,together with the associated effect of key process parameters on the quality of the final product.Based on the comprehensive literature conducted,it was found that:(1)indirect measurement,especially the data-based method,plays a critical role when it comes to online process control and quality assurance in nanomanufacturing,because of the short processing time compared to the direct method,and(2)physics-based method is providing a way to optimize the process set up for desired geometrical dimensions.
