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

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Partha Protim Mondal, Placid Ferreira, S. Kapoor, Patrick N. Bless
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引用次数: 0

摘要

作为一种广泛应用的人工智能工具,贝叶斯网络越来越多地用于多阶段制造过程的建模和故障诊断。然而,限制贝叶斯网络实际应用的主要问题是学习大型多阶段过程的网络结构的困难。传统上,贝叶斯网络结构要么是在领域专家的帮助下学习,要么是通过反复试验利用数据驱动的结构学习算法来学习。这两种方法都有其局限性。一方面,专家驱动的方法对于大型网络来说是昂贵、耗时、繁琐的,在评估概率时容易出错;另一方面,数据驱动的方法受到噪声、偏差、训练数据不足的影响,并且经常无法捕获数据的物理因果结构。因此,在本文中,我们提出了一种贝叶斯网络结构学习方法,该方法使用失效模式和影响分析(FMEA)和分层变量排序等流行的制造业知识来源作为结构先验来指导数据驱动的结构学习过程。此外,为了在学习过程中引入模块化和灵活性,我们提出了一种用于结构学习的顺序建模方法,以便大型多阶段网络可以逐步学习。此外,通过仿真研究,比较分析了基于知识源的结构偏差网络在多阶段过程故障诊断中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
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
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