Random Vibration Damage Detection for a Composite Beam Under Varying Non-measurable Conditions: Assessment of Statistica
The problem of vibration-response-only damage detection for a composite beam under variable and non-measurable Environmental and Operational Conditions (EOCs) is considered via three unsupervised Statistical Time Series (STS) type robust detection methods
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Abstract. The problem of vibration-response-only damage detection for a composite beam under variable and non-measurable Environmental and Operational Conditions (EOCs) is considered via three unsupervised Statistical Time Series (STS) type robust detection methods. These include three versions of a novel Functional Model (FM) based method, a Multiple Model (MM) based method, and a Principal Component Analysis (PCA) based method. Performance assessment is based on hundreds of inspection experiments under temperature ranging from 0 to 28 ◦ C and tightening torque ranging from 1 to 4 Nm. The results confirm the methods’ high effectiveness, with a version of the FM based method and the MM based method achieving ideal performance, characterized by 100% correct detection rate for 0% false alarm rate. Keywords: Robust damage detection · Structural Health Monitoring (SHM) · Vibration based methods Unsupervised methods · Composite structures · Uncertainty
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Introduction
Random vibration based Structural Health Monitoring (SHM) has rapidly progressed over the past several years, reaching high levels of technological maturity [1–3]. Statistical Time Series (STS) type methods [4], which employ corresponding models of the structural dynamics, are popular as they offer various advantages, including the exclusive use of data-based stochastic models which may be quite compact, only partially describing the dynamics. Yet, a major challenge relating to effective damage diagnosis under variable Environmental and Operating Conditions (EOCs) still remains. The fundamental reason behind it has to do with the fact that variable EOCs may affect the underlying structural dynamics to a degree that may be similar or even greater c Springer Nature Singapore Pte Ltd. 2020 M. A. Wahab (Ed.): DAMAS 2019, LNME, pp. 788–803, 2020. https://doi.org/10.1007/978-981-13-8331-1_62
Random Vibration Based Damage Detection
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than that caused by damage, and as such changes are at the core of damage diagnosis, the latter may become highly challenging and ineffective [5]. Overcoming this challenge requires the development of robust diagnosis methods, that are methods capable of ‘separating’, to the extent possible, the effects of variable EOCs from those of damage on the structural dynamics [5,6]. This generally requires the modeling of the considered dynamics under variable EOCs and uncertainty. Such modeling may assume various forms and be broadly classified—along with corresponding methods—as ‘explicit’ or ‘implicit’. ‘Implicit’ methods include Principal Component Analysis (PCA) [7] and Factor Analysis (FA) [8] based methods, while ‘explicit’ methods model the dynamics (for instance the ‘healthy’ structural dynamics in the context of damage detection) via explicit deterministic or stochastic modeling techniques and include Multiple Model (MM) [9], Random Coefficient (RC) model based [10], and the newly introduced Functional Model (FM) based methods [11–15]. Although assessment of individual methods are available, systematic and critical comparative as
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