Computation of Entropy Values for Intelligent Prognosis of Complex Machineries

In order to ensure the productivity and reliability of large-scale industrial sector, prognosis of machineries has been receiving higher attention in compare to the conventional diagnostic approach. This attention has been highly resonated lately by the researches based on the application of entropy theories such as Shannon entropy and its variants in the field of machine health prognosis. As statistical nonlinear measures, indices derived from entropy theories can characterize and quantify the machine health condition and its evolution in a continuous manner during its operation. Hence, entropy theory can be considered as a useful and reliable measure for developing and designing novel prognostic and health management techniques for complex machineries. Considering the aforementioned interest among the researchers, this keynote aims to present latest developments of entropy theory and its application to the prognostic and health management of complex industrial machineries.