Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods

Journal Article · Sun Jan 01 04:00:00 EST 2006 · Machining Science and Technology

DOI:https://doi.org/10.1080/10910340600996175· OSTI ID:931340

Liao, T. W. ^[1]; Hua, G ^[1]; Qu, Jun ^[2]; Blau, Peter Julian ^[2]

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1. Louisiana State University
2. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL); High Temperature Materials Laboratory
• Sponsoring Organization: EE USDOE - Office of Energy Efficiency and Renewable Energy (EE)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 931340
• Journal Information: Machining Science and Technology, Journal Name: Machining Science and Technology Journal Issue: 4 Vol. 10
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
ACOUSTIC EMISSION TESTING
ALGORITHMS
GRINDING MACHINES
MARKOV PROCESS
MONITORING