Paper Info
Title
Hidden Markov Model-Based Predictive Maintenance In Semiconductor Manufacturing: A Genetic Algorithm Approach
Abstract
The accuracy of data-mining based predictive maintenance often relies on extensive process and machine knowledge to enable appropriate feature selection and data preprocessing. Measurement data obtained may be asynchronous and result in inaccurate features, affecting the accuracy of maintenance prediction. To overcome this drawback, this paper introduces an approach to automatically select a feature subset through a genetic algorithm. The full feature set is created based on different sliding windows characterizing different time shifts on adopted statistical metrics of the measurement data. The fitness function of the genetic algorithm is then developed based on the preliminary fitting of a hidden Markov model (HMM) on the selected subset of features and assumed machines' condition in the training data. Ultimately the fittest subset of features is used to enable HMM-based predictive maintenance.The proposed approach is evaluated using data from semiconductor wafer production equipment, recorded over a period of one year.
Year
Venue
Keywords
2017
2017 13TH IEEE CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)
Condition monitoring, feature extraction, genetic algorithms, hidden Markov models, predictive maintenance, semiconductor device reliability
Field
DocType
ISSN
Asynchronous communication,Data mining,Feature selection,Computer science,Semiconductor device fabrication,Data pre-processing,Fitness function,Predictive maintenance,Hidden Markov model,Genetic algorithm
Conference
2161-8070
Citations 
PageRank 
References 
1
0.37
0
Authors
8
Name
Order
Citations
PageRank
Jakob Kinghorst111.05
Omid Geramifard2353.88
Ming Luo351.20
Hian-Leng Chan410.37
Khoo Yong510.37
Jens Folmer6448.37
Minjie Zou722.08
Vogel-Heuser, B.8521125.47