Online Adaptive Bearings Condition Assessment Using Continuous Hidden Markov Models

Condition assessment (CA) of bearings can be performed by using left-right hidden Markov models, because of the monotonically increasing pattern of the bearings degradation. Classical CA approaches assume that all possible system states are fixed and known a priori. The training of the system is performed offline at once with data from all of the system states. These assumptions significantly impede condition assessment applications in case that all the possible states of the system are not known in advance, or changes in environmental or operative conditions occur during the tool's usage. To overcome these limitations, we propose combining left-right continuous HMMs (CHMM) with a change point detection algorithm for (i) estimating, from historical observations, the initial number of the CHMM states and the initial guess of its model parameters and (ii) updating the state space as well as the model parameters during monitoring. Moreover, to deal with multidimensional sensor measurements, we propose using kernel principal component analysis for dimensionality reduction. Qualitative and quantitative evaluations of the proposed methodology have been performed using both simulated and real data from the NASA benchmark repository. Compared to state of the art techniques, the proposed methodology results in (i) an improvement of the HMM training phase in terms of iterations number; (ii) the detection of unknown states at an early stage; and (iii) an effective change of the CHMM's structure to represent the degradation processes more accurately in presence of unknown conditions