Partha Protim Mondal, Placid Ferreira, S. Kapoor, Patrick N. Bless
{"title":"用于识别多级过程监测和诊断的贝叶斯网络结构的序列建模和知识源集成","authors":"Partha Protim Mondal, Placid Ferreira, S. Kapoor, Patrick N. Bless","doi":"10.1115/1.4063235","DOIUrl":null,"url":null,"abstract":"\n As a popular applied artificial intelligence tool, Bayesian networks are increasingly being used to model multistage manufacturing processes for fault diagnosis purposes. However, the major issue limiting the practical adoption of Bayesian networks is the difficulty of learning the network structure for large multistage processes. Traditionally, Bayesian network structures are learned either with the help of domain experts or by utilizing data-driven structure learning algorithms through trial and error. Both approaches have their limitations. On one hand, expert-driven approach is costly, time-consuming, cumbersome for large networks, susceptible to errors in assessing probabilities and on the other hand, data-driven approaches suffer from noise, biases, inadequacy of training data and often fail to capture the physical causal structure of the data. Therefore, in this paper, we propose a Bayesian network structure learning approach where popular manufacturing knowledge sources like the Failure Mode and Effect Analysis (FMEA) and hierarchical variable ordering are used as structural priors to guide the data-driven structure learning process. In addition, to introduce modularity and flexibility into the learning process, we present a sequential modeling approach for structure learning so that large multistage networks can be learned stage by stage progressively. Furthermore, through simulation studies, we compare and analyze the performance of the knowledge source based structurally-biased networks in the context of multistage process fault diagnosis.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SEQUENTIAL MODELING AND KNOWLEDGE SOURCE INTEGRATION FOR IDENTIFYING THE STRUCTURE OF A BAYESIAN NETWORK FOR MULTISTAGE PROCESS MONITORING AND DIAGNOSIS\",\"authors\":\"Partha Protim Mondal, Placid Ferreira, S. Kapoor, Patrick N. Bless\",\"doi\":\"10.1115/1.4063235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n As a popular applied artificial intelligence tool, Bayesian networks are increasingly being used to model multistage manufacturing processes for fault diagnosis purposes. However, the major issue limiting the practical adoption of Bayesian networks is the difficulty of learning the network structure for large multistage processes. Traditionally, Bayesian network structures are learned either with the help of domain experts or by utilizing data-driven structure learning algorithms through trial and error. Both approaches have their limitations. On one hand, expert-driven approach is costly, time-consuming, cumbersome for large networks, susceptible to errors in assessing probabilities and on the other hand, data-driven approaches suffer from noise, biases, inadequacy of training data and often fail to capture the physical causal structure of the data. Therefore, in this paper, we propose a Bayesian network structure learning approach where popular manufacturing knowledge sources like the Failure Mode and Effect Analysis (FMEA) and hierarchical variable ordering are used as structural priors to guide the data-driven structure learning process. In addition, to introduce modularity and flexibility into the learning process, we present a sequential modeling approach for structure learning so that large multistage networks can be learned stage by stage progressively. Furthermore, through simulation studies, we compare and analyze the performance of the knowledge source based structurally-biased networks in the context of multistage process fault diagnosis.\",\"PeriodicalId\":16299,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4063235\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4063235","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
SEQUENTIAL MODELING AND KNOWLEDGE SOURCE INTEGRATION FOR IDENTIFYING THE STRUCTURE OF A BAYESIAN NETWORK FOR MULTISTAGE PROCESS MONITORING AND DIAGNOSIS
As a popular applied artificial intelligence tool, Bayesian networks are increasingly being used to model multistage manufacturing processes for fault diagnosis purposes. However, the major issue limiting the practical adoption of Bayesian networks is the difficulty of learning the network structure for large multistage processes. Traditionally, Bayesian network structures are learned either with the help of domain experts or by utilizing data-driven structure learning algorithms through trial and error. Both approaches have their limitations. On one hand, expert-driven approach is costly, time-consuming, cumbersome for large networks, susceptible to errors in assessing probabilities and on the other hand, data-driven approaches suffer from noise, biases, inadequacy of training data and often fail to capture the physical causal structure of the data. Therefore, in this paper, we propose a Bayesian network structure learning approach where popular manufacturing knowledge sources like the Failure Mode and Effect Analysis (FMEA) and hierarchical variable ordering are used as structural priors to guide the data-driven structure learning process. In addition, to introduce modularity and flexibility into the learning process, we present a sequential modeling approach for structure learning so that large multistage networks can be learned stage by stage progressively. Furthermore, through simulation studies, we compare and analyze the performance of the knowledge source based structurally-biased networks in the context of multistage process fault diagnosis.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining