Recommendation of PHM algorithms based on fuzzy information fusion

Abstract

The diversity of algorithms and the complication of application scenarios makes it difficult for PHM (Prognostics and Health Management) developers to accurately choose an appropriate algorithm when performing algorithm design. To make up for the aforementioned shortcoming, this paper proposes an algorithm recommendation system suitable for PHM. Specifically, according to the PHM architecture, a PHM database is designed to assist in the mining of recommended knowledge and the execution of recommended strategies. Subsequently, a recommendation system is developed that mainly includes hybrid data processing, similarity measurement, and recommendation decision making. Therein, two recommendation strategies, based on Fuzzy C-Means (FCM) method and weighted information fusion, are designed to cope with two kinds of actual requirements. Finally, the recommendation of remaining useful life (RUL) prediction associated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set is employed as an application case to verify the effectiveness of the proposed recommendation system.