An HMM-based change detection method for intelligent embedded sensors

In this work we address the problem of automatically detecting changes either induced by faults or concept drifts in data streams coming from multi-sensor units. The proposed methodology is based on the fact that the relationships among different sensor measurements follow a probabilistic pattern sequence when normal data, i.e. data which do not present a change, are observed. Differently, when a change in the process generating the data occurs the probabilistic pattern sequence is modified. The relationship between two generic data streams is modelled through a sequence of linear dynamic time-invariant models whose trained coefficients are used as features feeding a Hidden Markov Model (HMM) which, in turn, extracts the pattern structure. Change detection is achieved by thresholding the log-likelihood value associated with incoming new patterns, hence comparing the affinity between the structure of new acquisitions with that learned through the HMM. Experiments on both artificial and real data demonstrate the appreciable performance of the method both in terms of detection delay, false positive and false negative rates