A probabilistic multi-class classifier for structural health monitoring. (August 2015)

• Record Type: Journal Article
• Authors: Mechbal, Nazih; Uribe, Juan Sebastian; Rébillat, Marc
• Is Part Of: Mechanical systems and signal processing. Volume 60/61(2015)
• Page Start: 106
• Page End: 123
• Publication Date: 2015-08
• Subjects: Structural health monitoring -- Support vector machine -- Probabilistic decision tree -- Pattern recognition -- Wave diffraction patterns -- Piezoelectric elements; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.01.017
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: In this paper, a probabilistic multi-class pattern recognition algorithm is developed for damage monitoring of smart structures. As these structures can face damages of different severities located in various positions, multi-class classifiers are needed. We propose an original support vector machine (SVM) multi-class clustering algorithm that is based on a probabilistic decision tree (PDT) that produces a posteriori probabilities associated with damage existence, location and severity. The PDT is built by iteratively subdividing the surface and thus takes into account the structure geometry. The proposed algorithm is very appealing as it combines both the computational efficiency of tree architectures and the SVMs classification accuracy. Damage sensitive features are computed using an active approach based on the permanent emission of non-resonant Lamb waves into the structure and on the recognition of amplitude disturbed diffraction patterns. The effectiveness of this algorithm is illustrated experimentally on a composite plate instrumented with piezoelectric elements. Highlights: We developed a probabilistic multi-class pattern recognition algorithm. We provide confidence probabilities associated with the location of each damage. Availability of damage probability facilitates embedded damage monitoring. The geometry of the structure is introduced to enhance the classification. We localize and quantify the severity of a damage experimentally.