Novelty Detection Based Machine Health Prognostics

Abstract

In this paper we present a new novelty detection algorithm for continuous real time monitoring of machine health and prediction of potential machine faults. The kernel of the system is a generic evolving model that is not dependent on the specific measured parameters determining the health of a particular machine. Two alternative strategies are introduced in order to predict abrupt and gradually developing (incipient) changes. This algorithm is realized as an autonomous software agent that continuously updates its decision model implementing an unsupervisory recursive learning algorithm. Results of validation of the proposed algorithm by accelerated testing experiments are also discussed