GRINDING WHEEL CONDITION MONITORING WITH HIDDEN MARKOV MODEL-BASED CLUSTERING METHODS |
| |
Authors: | T Warren Liao Guogang Hua J Qu P J Blau |
| |
Affiliation: | 1. Industrial Engineering Department , Louisiana State University , Baton Rouge , Louisiana , USA ieliao@lsu.edu;3. Industrial Engineering Department , Louisiana State University , Baton Rouge , Louisiana , USA;4. Metals and Ceramics Division , Oak Ridge National Laboratory , Tennessee , USA |
| |
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. |
| |
Keywords: | Grinding Wheel Condition Monitoring Hidden Markov Model Sequence Data Clustering Algorithm Dissimilarity Measure |
|
|