1. 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&verbar;j), wherein state 1 applies when the number of said occurrences exceeds a certain threshold T, and state 0 applies when the number of said occurrences falls below said certain threshold T, being represented as: 2 S n = { 0 if x n &leq; T 1 if x n > T wherein said transition probability p(i&verbar;j) is the switching probability from state j to state i, that is, the probability that S n &equals;i given that S n−1 &equals;j, being a total of 4 transition probabilities; computing said four transition probabilities the 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&verbar;1) and p(1&verbar;0), said training session comprising: computing the two-dimensional feature vectors f i &equals;&lcub;p(1&verbar;1), p(1&verbar;0)&rcub; i for the initial M windows of N scans, computing the two-dimensional feature vectors f f &equals;&lcub;p(1&verbar;1), p(1&verbar;0) f for the final N number of scans, plotting a scatter-diagram of all 2D feature vectors (f i ) n and (f f ) n, (n&equals;1... J), and deriving a pattern classifier by estimating the optimal linear discriminant which separates the two foregoing sets of vectors; and applying said classifier to monitor the persistence of occurrences of said defined event in the operation of said device.