A temperature-driven MPCA method for structural anomaly detection. (1st July 2019) - Zhu, Yanjie; Ni, Yi-Qing; Jin, Hui; Inaudi, Daniele; Laory, Irwanda
Highlights: A temperature-driven damage detection method is presented, abbreviated as Td-MPCA. The Td-MPCA method is to separate thermal effect for anomaly detection. Blind source separation is introduced for thermal identification. The outperformance of proposed Td-MPCA can be demonstrated in this study. Abstract: An important issue in structural health monitoring (SHM) is to develop appropriate algorithms that can explicitly extract meaningful changes in measurements due to structural anomalies, especially damage. However, the effects due to environmental factors, especially temperature variations may produce significant misinterpretations. Consequently, developing solutions to identify the structural anomaly, accounting for temperature influence, from measurements, is crucial and highly anticipated. This paper presents a Temperature-driven Moving Principal Component Analysis method, designated as Td-MPCA, for anomaly detection. The Td-MPCA introduces the idea of blind source separation (BSS) for thermal identification with intent to enhance the performance of Moving Principal Component Analysis (MPCA) for anomaly detection. To achieve this target, temperature-induced strain variations are first investigated and revealed by employing Independent Component Analysis based on maximization non-Gaussianity, also known as Fast ICA. Afterwards, the MPCA is adopted for anomaly detection on the separated temperature-related response. Three case studies are provided in this paper to evaluate the proposed method. The first one is a numerical truss bridge with a simulated 5% stiffness reduction. The results confirm that Td-MPCA is more sensitive than MPCA in detecting anomalies, where the simulated stiffness loss fails to be detected by MPCA. The second case study is on an experimental truss bridge where two damage scenarios are introduced and interpreted. The detection results show that Td-MPCA outperforms MPCA since the damage is identified at the expected time by Td-MPCA but not by MPCA. The third case study is an in-situ curved viaduct in Switzerland. Data acquired during both construction period and normal service period has been used for interpretation. Results demonstrate that Td-MPCA is able to identify the date of change in construction process without any delay when compared with the application of MPCA only.