Fusing diverse monitoring algorithms for robust change detection

Change detection is an important task in remotely monitoring and diagnosing equipment and other processes. Specifically, early detection of differences that indicate abnormal conditions has the promise to provide considerable savings in averting secondary damage and preventing system outage. Of course, accurate early detection has to be balanced against the successful rejection of false positive alarms. In noisy environments, such as aircraft engine monitoring, this proves to be a difficult undertaking for any one algorithm. In this paper, we investigate the performance improvement that can be gained by aggregating the information from a set of diverse change detection algorithms. Specifically, we examine a set of change detectors that utilize a variety of different techniques such as neural nets, random forests, and support vector machines. The different techniques have different detection sensitivities and different false positive rates. For fusion, we consider the Dempster regression technique that operates well for time series as well as averaging schemes, and a meta-classifiers. We provide results using illustrative examples from aircraft engine monitoring