Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods
Abstract
Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet decomposition level, wavelet basis, clustering algorithm, and data normalization were also studied.
- Authors:
-
- Louisiana State University
- ORNL
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); High Temperature Materials Laboratory
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 931340
- DOE Contract Number:
- DE-AC05-00OR22725
- Resource Type:
- Journal Article
- Resource Relation:
- Journal Name: Machining Science and Technology; Journal Volume: 10; Journal Issue: 4
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 97; GRINDING MACHINES; MARKOV PROCESS; ALGORITHMS; MONITORING; ACOUSTIC EMISSION TESTING
Citation Formats
Liao, T. W., Hua, G, Qu, Jun, and Blau, Peter Julian. Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods. United States: N. p., 2006.
Web. doi:10.1080/10910340600996175.
Liao, T. W., Hua, G, Qu, Jun, & Blau, Peter Julian. Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods. United States. doi:10.1080/10910340600996175.
Liao, T. W., Hua, G, Qu, Jun, and Blau, Peter Julian. Sun .
"Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods". United States.
doi:10.1080/10910340600996175.
@article{osti_931340,
title = {Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods},
author = {Liao, T. W. and Hua, G and Qu, Jun and Blau, Peter Julian},
abstractNote = {Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet decomposition level, wavelet basis, clustering algorithm, and data normalization were also studied.},
doi = {10.1080/10910340600996175},
journal = {Machining Science and Technology},
number = 4,
volume = 10,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2006},
month = {Sun Jan 01 00:00:00 EST 2006}
}
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