Knowledge-based linguistic equations for defect detection through functional testing of printed circuit boards

Gebus, Sébastien; Juuso, Esko; Leiviskä, Kauko

doi:10.1016/j.eswa.2007.09.059

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…The approach improves sensitivity for normal, Poisson and Weibull distributions [20]. This is beneficial in condition monitoring [20] and defect detection [41]. The scaling functions can be tuned with genetic algorithms [19].…”

Section: Nonlinear Scalingmentioning

confidence: 99%

Intelligent performance measures for condition‐based maintenance

Juuso

Lahdelma

2013

Journal of Quality in Maintenance Engineering

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Advanced data analysis is needed to efficiently integrate maintenance and operation. A good basis for this increasingly important task is process and condition monitoring data. Intelligent stress, condition and health indicators have been developed for control and condition monitoring by combining generalised moments and norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management The data-driven analysis methodology demonstrates that management oriented indicators can be presented in the same scale as intelligent condition and stress indices.

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Section: Nonlinear Scalingmentioning

confidence: 99%

Intelligent performance measures for condition‐based maintenance

Juuso

Lahdelma

2013

Journal of Quality in Maintenance Engineering

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“…Those articles which do refer to the manufacturing test environment do not vary significantly from the more general fault diagnosis literature. Two specific examples are the use of linguistic equations based on fuzzy sets in the electronics sector (Gebus et al , 2009) and the use of a hybrid intelligent system employing both CBR and rule‐based reasoning in the Taiwanese steel industry (Wang and Wang, 2005). A model‐based approach employing maximum likelihood estimation to diagnose process faults is described by Du and Xi (2007) in the context of a serial machining manufacturing system.…”

Section: Literature Reviewmentioning

confidence: 99%

Bayesian networks in manufacturing

McNaught

Chan

2011

Journal of Manufacturing Technology Management

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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.

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Evaluating record history of medical devices using association discovery and clustering techniques

Miguel-Cruz

2013

Expert Systems with Applications

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Knowledge-based linguistic equations for defect detection through functional testing of printed circuit boards

Cited by 7 publications

References 20 publications

Intelligent performance measures for condition‐based maintenance

Intelligent performance measures for condition‐based maintenance

Bayesian networks in manufacturing

Evaluating record history of medical devices using association discovery and clustering techniques

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