Bayesian networks in manufacturing

Abstract

PurposeThe purpose of this paper is to raise awareness among manufacturing researchers and practitioners of the potential of Bayesian networks (BNs) to enhance decision making in those parts of the manufacturing domain where uncertainty is a key characteristic. In doing so, the paper describes the development of an intelligent decision support system (DSS) to help operators in Motorola to diagnose and correct faults during the process of product system testing.Design/methodology/approachThe intelligent (DSS) combines BNs and an intelligent user interface to produce multi‐media advice for operators.FindingsSurveys show that the system is effective in considerably reducing fault correction times for most operators and most fault types and in helping inexperienced operators to approach the performance levels of experienced operators.Originality/valueSuch efficiency improvements are of obvious value in manufacturing. In this particular case, additional benefit was derived when the product testing facility was moved from the UK to China as the system was able to help the new operators to get close to the historical performance level of experienced operators.

Enhancing the Product Realization Process With Cloud-Based Design and Manufacturing Systems

“…Since introduced in 1980s by Pearl [64], Bayesian networks (BNs) have become increasingly popular to represent and discover knowledge. McNaught and Chan [65] discuss the potential application of BNs in manufacturing, with a focus on the development of an intelligent decision support system to aid fault diagnosis and correction during product system testing. Li and Shi [66] present a causal modeling approach to discover the causal relationships among the product quality and process variables in a rolling process by integrating manufacturing domain knowledge with the generic learning algorithm.…”

Building a maintenance policy through a multi-criterion decision-making model

“…Some authors such as Jorgenson et al (1967), McCall (1965, Dayanlk and Gurler (2002), Wilson and Benmerzouga (1995), Sheu et al (2001), Juang and Anderson (2004), Kallen and Van Noortwijk (2005), Makis and Jardine (1992), McNaught and Chan (2011), and many others have used this approach in different maintenance models (Oberschmidt et al 2010).…”

Performance evaluation of a manufacturing process under uncertainty using Bayesian networks

“…The above research has focused mostly on the analysis of physical systems, not manufacturing environments involving multiple processes. So far, Bayesian networks have been used in the manufacturing domain for fault diagnosis (McNaught and Chan;2011, Rodrigues et al;2000) and discrete-event reliability modeling (Weber and Jouffe;, but the focus in this paper is on information fusion, calibration of uncertain parameters, uncertainty reduction and handling of both discrete and continuous variables for performance prediction. Note that fault diagnostics and monitoring are focused on measurement and inference about the current state.…”