Applications of probability density estimation to the detection of abnormal conditions in engineering

Abstract

A method is described for the identification of abnormal or unexpected conditions from measured response data. Such a technique would be useful in a wide range of engineering situations where a clear, early warning of an abnormal condition is required, but where classification of the specific abnormality is only of secondary importance. In this work, occurrences of unexpected operating conditions are indicated by measured data which exhibit a high degree of novelty with respect to that corresponding to normal conditions or responses. The proposed approach is based upon the probability density function (PDF) estimation using a kernel method, the basis of which is described. The need for data compression in practical applications of PDF estimation is highlighted and a method demonstrated which is based on the wavelet transform. The combined data compression and PDF estimation approach for novelty detection is applied to data measured from a gearbox with a progressive fault and to radar data corresponding to six military targets. In both cases, abnormal situations are clearly identified on the basis of novel data inputs.