Abstract
Abstract In recent years, machine learning (ML) techniques have gained popularity in structural health monitoring (SHM). These have been particularly used for damage detection in a wide range of engineering applications such as wind turbine blades. The outcomes of previous research studies in this area have demonstrated the capabilities of ML for robust damage detection. However, the primary challenge facing ML in SHM is the lack of interpretability of the prediction models hindering the broader implementation of these techniques. For this purpose, this study integrates the novel Shapley Additive exPlanations (SHAP) method into a ML-based damage detection process as a tool for introducing interpretability and, thus, build evidence for reliable decision-making in SHM applications. The SHAP method is based on coalitional game theory and adds global and local interpretability to ML-based models by computing the marginal contribution of each feature. The contribution is used to understand the nature of damage indices (DIs). The applicability of the SHAP method is first demonstrated on a simple lumped mass-spring-damper system with simulated temperature variabilities. Later, the SHAP method has been evaluated on data from an in-operation V27 wind turbine with artificially introduced damage in one of its blades. The results show the relationship between the environmental and operational variabilities (EOVs) and their direct influence on the damage indices. This ultimately helps to understand the difference between false positives caused by EOVs and true positives resulting from damage in the structure.
Highlights
Structural health monitoring (SHM) refers to the process of identifying changes in the integrity of a structure based on observations and measurements that describe its current state
Among the diversity of methods adopted by researchers, data-driven vibration-based structural health monitoring (VSHM) is a promising approach and is continuously being studied
The primary goal of VSHM is to detect damage in a structure through the following steps [1]: (1) Measure vibration responses from the structure; (2) Extract damage sensitive features (DSFs), i.e. metrics that contain useful information to characterize the state of the structure; (3) Select a method to process the DSFs and calculate a damage index (DI); and (4) compare the DI with a predefined threshold, thereby identifying outliers that indicate potential damage in the structure
Summary
Structural health monitoring (SHM) refers to the process of identifying changes in the integrity of a structure based on observations and measurements that describe its current state. This approach outperformed the support vector machine method when dealing with multi-dimensional features sets in terms of avoiding model overfitting.Solimine et al [13] used principal component analysis (PCA) and K-means clustering to identify outliers, i.e., deviations from the normal operation of a full-scale wind turbine blade (WTB), through the collection of audio signals from the blade cavity Their method successfully detected structural and acoustic abnormalities when the WTB underwent fatigue testing. For SHM applications, Lim et al [22] estimated different damage levels for several bridges based on monitored conditions of their decks using an XGBoost classification model Their data was retrieved from the Korean Bridge Management System and corresponded to 142,439 deck inspection records of 2388 bridges. The process is complemented with the SHAP approach for enhanced understanding of the variabilities in the DIs identified This post-detection interpretability enables explainability in online SHM systems.
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