TY - GEN
T1 - Hidden Markov model-based predictive maintenance in semiconductor manufacturing
T2 - 13th IEEE Conference on Automation Science and Engineering, CASE 2017
AU - Kinghorst, Jakob
AU - Geramifard, Omid
AU - Luo, Ming
AU - Chan, Hian Leng
AU - Yong, Khoo
AU - Folmer, Jens
AU - Zou, Minjie
AU - Vogel-Heuser, Birgit
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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 semi-conductor wafer production equipment, recorded over a period of one year.
AB - 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 semi-conductor wafer production equipment, recorded over a period of one year.
KW - Condition monitoring
KW - feature extraction
KW - genetic algorithms
KW - hidden Markov models
KW - predictive maintenance
KW - semiconductor device reliability
UR - https://www.scopus.com/pages/publications/85044946205
U2 - 10.1109/COASE.2017.8256274
DO - 10.1109/COASE.2017.8256274
M3 - Conference contribution
AN - SCOPUS:85044946205
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1260
EP - 1267
BT - 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PB - IEEE Computer Society
Y2 - 20 August 2017 through 23 August 2017
ER -