Bayesian Belief Network-based approach for diagnostics and prognostics of semiconductor manufacturing systems

Yang, Lei; Lee, Jay

doi:10.1016/j.rcim.2011.06.007

Cited by 87 publications

(30 citation statements)

References 28 publications

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“…This assumption brings extra computation and also yields erroneous estimation in theoretical information measures in structure learning (Yang et al, 2013). Yang & Lee (2012) demonstrated the linear impact of improvement in model quality within the scope of exercising BIC function score in K2 (Cooper & Herskovits, 1992).…”

Section: September 2013mentioning

confidence: 97%

A novel mutual dependence measure in structure learning

Naeem

Asghar

2013

J. Natn. Sci. Foundation Sri Lanka

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Mutual dependence between features plays an important role in the formulation of classifiers, clustering and other machine intelligent techniques. In this study a novel measure of mutual information known as integration to segregation (I2S), explaining the relationship between the two features is proposed. Some important characteristics of the proposed measure was investigated and its performance in terms of class imbalance measures was compared. It was shown that I2S possesses the characteristics, which are useful in controlling overfitting problems. In structure learning techniques such as Bayesian belief networks, conventional measures of dependency relationship cope with the overfitting problem by restricting the number of parents for a node; however it is still not impressive because complete overfitting is not eliminated. In contrast, I2S is capable of significantly maximizing the discriminant function with a better control of overfitting in the formulation of structure learning.

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Section: September 2013mentioning

confidence: 97%

A novel mutual dependence measure in structure learning

Naeem

Asghar

2013

J. Natn. Sci. Foundation Sri Lanka

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“…The precise construction of the tree structure of a BN from data is an NP-hard optimization problem [8]. Yang & Lee [9] and Correa et al [10] made use of K2 [11] and Chow-Liu [12], algorithms respectively to generate trees from data. Jeong et al [13] extract the cause-effect relationship for the equipment that is diagnosed from the equipment's maintenance manual.…”

Section: Bayesian Networkmentioning

confidence: 99%

“…The areas of application of BN vary across manufacturing industries. In the semiconductor industry, Yang & Lee [9] and Nguyen et al [6] used a BN to evaluate process variable influence on wafer quality to diagnose root cause of defective wafers using historic process data. Other application areas include the automobile industry [7] where BN is used to diagnose fixture fault in a taillight assembly and in machining [10] where BN is used to diagnose surface roughness fault.…”

Section: Bayesian Networkmentioning

confidence: 99%

A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

Ademujimi

Brundage

Prabhu

2017

IFIP Advances in Information and Communication Technology

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Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.

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“…The causal variable analysis is main application of Bayesian network when the observed statues on any of the random variables are given. Conditional probability of unobserved modes is updated through belief propagation and inference can be made about the most probable status [32]. In the example of Figure 10, if variable is observed as true, the statues of off6 can be inferred from this evidence such that the probability off6| ( , ture) is needed for this inference, and it can be quantified by marginalizing the joint distribution under the condition that the status of is known:…”

Section: Fault Prognosis Schemementioning

confidence: 99%

Bayesian Network Based Fault Prognosis via Bond Graph Modeling of High-Speed Railway Traction Device

Jiang

et al. 2015

Mathematical Problems in Engineering

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Reliability of the traction system is of critical importance to the safety of CRH (China Railway High-speed) high-speed train. To investigate fault propagation mechanism and predict the probabilities of component-level faults accurately for a high-speed railway traction system, a fault prognosis approach via Bayesian network and bond graph modeling techniques is proposed. The inherent structure of a railway traction system is represented by bond graph model, based on which a multilayer Bayesian network is developed for fault propagation analysis and fault prediction. For complete and incomplete data sets, two different parameter learning algorithms such as Bayesian estimation and expectation maximization (EM) algorithm are adopted to determine the conditional probability table of the Bayesian network. The proposed prognosis approach using Pearl’s polytree propagation algorithm for joint probability reasoning can predict the failure probabilities of leaf nodes based on the current status of root nodes. Verification results in a high-speed railway traction simulation system can demonstrate the effectiveness of the proposed approach.

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Bayesian Belief Network-based approach for diagnostics and prognostics of semiconductor manufacturing systems

Cited by 87 publications

References 28 publications

A novel mutual dependence measure in structure learning

A novel mutual dependence measure in structure learning

A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

Bayesian Network Based Fault Prognosis via Bond Graph Modeling of High-Speed Railway Traction Device

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