Damage and fault detection of structures using principal component analysis and hypothesis testing
Document typePart of book or chapter of book
Rights accessRestricted access - publisher's policy
This chapter illustrates the application of principal component analysis (PCA) plus statistical hypothesis testing to online damage detection in structures, and to fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults. A baseline pattern or PCA model is created with the healthy state of the structure using data from sensors. Subsequently, when the structure is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data are compared, both univariate and multivariate statistical hypothesis testing is used to make a decision. In this work, both experimental results (with a small aluminum plate) and numerical simulations (with a well-known benchmark wind turbine) show that the proposed technique is a valuable tool to detect structural changes or faults.
CitationPozo, F., Vidal, Y. Damage and fault detection of structures using principal component analysis and hypothesis testing. A: "Advances in Principal Component Analysis". Berlín: Springer, 2017, p. 137-191.