Autonomous diagnostics and prognostics in machining processes through competitive learning-driven HMM-based clustering

A prerequisite to widespread deployment of condition-based maintenance (CBM) systems in industry is autonomous yet effective diagnostics and prognostics algorithms. The concept of 'autonomy' in the context of diagnostics and prognostics is usually based on unsupervised clustering techniques. This paper employs an unsupervised competitive learning algorithm to perform hidden Markov model (HMM) based clustering of multivariate temporal observation sequences derived from sensor signal(s). This method improves autonomy of the diagnostic engine over the more traditional classifier based diagnostics models. Classifier models, such as the model presented by Baruah and Chinnam [Baruah, P. and Chinnam, R. B., 2005. HMM for diagnostics and prognostics in machining processes. International Journal of Production Research, 43 (6), 1275-1293] employ 'labelled' feature vectors for supervised model building and subsequent health-state classification during condition monitoring. Improving the autonomy of the diagnostics model also improves the autonomy of the prognostics module that often builds upon information extracted through the diagnostics module. We have validated the proposed methods on a physical test-bed that involved monitoring drill-bits on a CNC machine outfitted with thrust-force and torque sensors. Experimental results demonstrate the ability of this method to estimate on-line the remaining-useful-life of a drill-bit with significant accuracy.