A multi-modal hidden Markov model based approach for continuous health assessment in machinery systems

In this paper¹, a multi-modal approach based on the single hidden Markov model (HMM) with continuous output is introduced for continuous health condition monitoring in machinery systems. Comparing with existing approaches such as single HMM-based approach, artificial neural networks (ANN) approach, auto-regressive moving average with exogenous inputs (ARMAX), the proposed approach improves the performance of health condition monitoring (HCM) by using multiple HMM models in parallel. Each model emphasizes on different regiments, and outputs of all models are integrated as the ultimate output. The integration of HMM outputs are conducted by either a parametric or a semi-nonparametric hindsight method. The proposed approach is applied to tool wear prediction of a CNC-milling machine, and results are compared with an existing HMM-based approach.