Fault Detection Assessment Architectures based on Classification Methods and Information Fusion

Abstract

Classifiers based on machine learning are popular in literature, in order to support predictive maintenance of machinery. Depending on the process data, one classifier can assess target classes better than others. It often happens that the classifiers complement each other. A fusion strategy is needed in order to exploit the strength of each classifier. This paper presents fault detection assessment architectures based on information fusion and classification methods. It proposes the use of information fusion methods and different architectures, in order to improve the overall result of the fault detection assessment. Dempster-Shafer and Yager rules of combination are used to fuse the classification method predictions. The rules of combination improve the results by complementing the classifiers performance. A comparison between centralized and decentralized architectures is presented. The results show that the information fusion using decentralized architectures improves the overall performance of the fault detection assessment.