Bayesian Network Structure Learning Algorithms of Optimizing Fault Sample Set

Guo, Hongyang; Zhang, Rupei; Yong, Jiaqi; Jiang, Bin

doi:10.1007/978-3-662-48386-2_34

Cited by 4 publications

(1 citation statement)

References 3 publications

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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%

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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