Patent ID: 6721445
Filing Date: 2004-04-13
Classification: G06F

Abstract:
A method for detecting anomalies in a digitized complex signal analyzed by a detection unit, including a machine learning step including a parameterizing of an automatic compression system, and a step of diagnosis of the intensity and/or the rarity of an anomaly,the learning including the steps of: 1.1 selecting a succession of sequences of values of the analyzed signal corresponding to a succession of time windows (Fk); 1.2 transforming the signal of each of the windows to extract therefrom characteristics of a type able to be extracted by a human eye to form a first vector (Dk) of dimension n; 1.3 reducing number n of digital data by an automatic compression of the first vector (Dk) to provide a second vector (IDk) with coordinates substantially independent in probabilistic terms, of dimension p smaller than n; 1.4 calculating, for j varying from 1 to p, a histogram Hj of each coordinate of the second vectors (IDk), calculating for each of these coordinates the probability Pj(a) for this coordinate to be greater than a, if a is greater than the median of the histogram Hj, or smaller than a, if a is smaller than the median of the histogram Hj, and determining a function Zj(a)=âˆ’log[Pj(a)], the diagnosis including the steps of: 2.1 applying steps 1.1 to 1.3 to a polling window (Fk) likely to include an anomaly; 2.2 calculating the sum over j for this polling window (Fk) so as to obtain a score of abnormality R=&Sgr;Zj(IDkj); and 2.3 comparing said sum (R) with an intensity or rarity threshold predefined by the user.