Fault Detection Using Principal Components-Based Gaussian Mixture Model for Semiconductor Manufacturing Processes

2011 ◽  
Vol 24 (3) ◽  
pp. 432-444 ◽  
Author(s):  
Jianbo Yu
Keyword(s):  
Fault Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Principal Components ◽  
Semiconductor Manufacturing ◽  
Gaussian Mixture ◽  
Manufacturing Processes

scholarly journals Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

2020 ◽  
Vol 66 (4) ◽  
pp. 215-226 ◽  
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Fault Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Condition Monitoring ◽  
Gaussian Mixture ◽  
Classification Method ◽  
Fault Classification ◽  
Classification Methods ◽  
Bearing Fault ◽  
Model Based

Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring.

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