Patent ID: 6847918

Claim:
A method for providing predictive maintenance of a device, comprising the steps of: modeling as a time series x n of a discretely sampled signal representative of occurrences of a defined event in the operation of said device, said time series x n being modeled as two-state first order Markov processes with associated transition probabilities p(i|j), wherein state 1 applies when a number of said occurrences exceeds a certain threshold T, and state 0 applies when a number of said occurrences falls below said certain threshold T, being represented as: S n = { 0 if x n ≤ T 1 if x n > T wherein said transition probabilities p(i|j) are switching probabilities from state j to state i, characterized by the probability that S n =i given that S n−1 =j, being a total of four transition probabilities; computing said four transition probabilities for a last N states S n , where N is a predetermined number; conducting a supervised training session utilizing a set of J devices, which have failed due to known causes and considering the two independent probabilities p(1|1) and p(1|0), said training session comprising: computing two-dimensional feature vectors f i ={p(1|1), p(1|0)} i for an initial M windows of N scans, thereby a first vector set, computing two-dimensional feature vectors f f ={p(1|1), p(1|0) f for o final N number of scans, thereby forming a second vector set, plotting a scatter-diagram of all two-dimensional feature vectors (f i ) n and (f f ) n , (n=1 . . . J), and deriving from the scatter-diagram a pattern classifier by estimating an optimal linear discriminant which separates the first and second vector sets; and applying said classifier to monitor a persistence of occurrences of said defined event in an operation of said device.