A probabilistic multi-class classifier for structural health monitoring

Abstract

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. In this paper, a probabilistic multi-class pattern recognition algorithm is developed for damage detection, localization, and quantification in smart mechanical structures. As these structures can face damages of different severities located at various positions, multi-class classifiers are naturally needed in that context. Furthermore, because of the lack of available data in the damaged state and of environmental effects, the experimentally obtained damage sensitive features may differ from those learned offline by the classifier. A multiclass classifier that provides probabilities associated with each damage severity and location instead of a binary decision is thus greatly desirable in that context. To tackle this issue, we propose an original support vector machine (SVM) multi-class clustering algorithm that is based on a probabilistic decision tree (PDT) and that produces a posteriori probabilities associated with damage existence, location, and severity. Furthermore, the PDT is here built by iteratively subdividing the surface of the structure and thus takes into account the actual structure geometry. The proposed algorithm is very appealing as it combines both the computational efficiency of tree architectures and the classification accuracy of SVMs. The effectiveness of this algorithm is illustrated experimentally on a composite plate instrumented with piezoelectric elements on which damages are simulated using added masses. 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. On the basis of these damage-sensitive features, the proposed multi-class probabilistic classifier generates decisions that are in excellent agreement with the actual severities and locations of the simulated damages.

Detection of structural damage and estimation of reliability using a multidimensional monitoring approach

“…The method proposed herein differs from the machine learning methods as they learn from examples, an action which limits the type of identifiable failures. 55,56 The proposed method estimates the current reliability of the system based on several lowly correlated symptoms and also finds the source of data that contributes the most to condition variation. Finally, the method explored in this article relates condition evolution with system reliability.…”

Extrapolation of AR models using cubic splines for damage progression evaluation in composite structures

“…First, damage detection and localization using an index extracted by predictions errors filtered with a reference AR model are performed handling conventional tools. Any classifier could be used in this step of the procedure, for example, support vector machine or other probabilistic machine learning algorithms (Mechbal et al, 2015). However, the goal of the present paper is not to study the classifier in itself.…”

Impact Localization Method for Composite Plate Based on Low Sampling Rate Embedded Fiber Bragg Grating Sensors

“…Structural Health Monitoring (SHM) is a new kind of nondestructive testing (NDT) techniques that allow realtime assessment of structural integrity and durability and hence provide early warning for structure safety [1,2]. SHM has recently gained growing interests because of its high reliability and low cost that meets the increasing requirement of many applications, especially in the field of aerospace and civil engineering [1][2][3][4].…”

“…SHM has recently gained growing interests because of its high reliability and low cost that meets the increasing requirement of many applications, especially in the field of aerospace and civil engineering [1][2][3][4]. In SHM applications, data acquisition system plays an important role.…”