An artificial neural network methodology for damage detection: Demonstration on an operating wind turbine blade

This study presents a novel artificial neural network (ANN) based methodology within a vibration-based structural health monitoring framework for robust damage detection. The ANN-based methodology establishes the nonlinear relationships between selected damage sensitive features (DSF) influenced by environmental and operational variabilities (EOVs) and their corresponding novelty indices computed by the Mahalanobis distance (MD). The ANN regression model is trained and validated based on a reference state (i.e., a healthy structure). The trained model is used to predict the corresponding MD of new observations. The prediction error between the calculated and predicted MD is used as a new novelty index for damage detection. Firstly, an artificial 2D feature set is generated to illustrate how the limitations of solely using the MD-based novelty index can be overcome by the proposed ANN-based methodology. Secondly, the methodology is implemented in data obtained from an in-operation wind turbine with different artificially induced damage scenarios in one of its blades. Finally, the performance of the proposed methodology is evaluated by the metrics of accuracy, F1-score and Matthews correlation coefficient. The results demonstrate the advantages of the proposed methodology by improving damage detectability in all the different damage scenarios despite the influence of EOVs in both the simulated and real data. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study presents a novel artificial neural network (ANN) based methodology for robust damage detection within a vibration-based structural health monitoring framework. The method establishes nonlinear relationships between damage sensitive features and novelty indices, using prediction error as a new index. The results demonstrate improved damage detectability in various scenarios, despite the influence of environmental and operational variables.