Monitoring, detection, and surveillance system using principal component analysis with machine and sensor data

Abstract

A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector scomputed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector shas a dimension equal to m a number of the plurality of variables. A zero counter time series value ĉis computed using ĉ=Σs[i]. A probability value for ĉis computed using p=ΣH[i]/ΣH[i], where H[i] includes a count of a number of times each value of ĉoccurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.