An unsupervised learning damage diagnosis method based on virtual impulse response function and time series models. (April 2023)

• Record Type: Journal Article
• Authors: Zhang, Xuan; Li, Luyu
• Is Part Of: Measurement. Volume 211(2023)
• Publication Date: 2023-04
• Subjects: Data-driven -- Unsupervised learning -- Damage detection -- Time series model -- Virtual impulse response function -- Ambient excitations; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2023.112635
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: As it contains the inherent properties of the structure and is insensitive to variation of the input, the transmissibility function (TF) is widely used for structural damage detection under ambient excitation. However, in TF-based methods, the selection of the optimal damage sensitivity band often requires access to vibration data under different operating conditions, which is not conducive to unsupervised learning. In order to accommodate unsupervised learning and extract more damage information, this paper proposes a data-driven unsupervised damage detection method based on the virtual impulse response function (VIRF) and time series model (TSM). In this method, the VIRF is used as a damage-sensitive feature (DSF), the TSM is used for low-dimensional feature extraction of the VIRF, and the distance metric of the model coefficients is used to detect the occurrence of anomalies. The study analyses the type of TSM applied to VIRF and uses the parameter adjusted cosine similarity (PACS) as a more accurate statistical distance tool for the model coefficients. The application of two well-known benchmark structure experiments verifies the accuracy and practicality of the proposed method in damage detection under minor operational variations and ambient excitations. The comparative analysis and detection results indicate that the proposed method is advantageous in qualitatively quantifying the extent of the damage. Structural anomalies can be detected in both linear and nonlinear damage scenarios. Highlights: Eliminates the need to acquire damage signals in advance of the baseline training phase. Applicable to ambient excitation and advantageous in quantifying the extent of damage. Abnormalities in the structure can be detected in both linear and non-linear damage scenarios. Potential for comprehensive damage assessment of structures.