PSI - Issue 37

Deniss Mironovs et al. / Procedia Structural Integrity 37 (2022) 410–416 Deniss Mironovs/ Structural Integrity Procedia 00 (2019) 000 – 000

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1. Introduction In the past 30 years with the development of non-destructive testing methods structural health monitoring has been rapidly developing in efficiency, practicality, and precision. There are numerous SHM approaches, vibration-based is being one of them (Trendafilova et al. (2008), Weijtjens et al. (2017)). Vibration-based methods are usually based on modal assessment of an investigated structure. It relies on the fact that if a change is introduced into a structure, then this change modifies its physical properties and thus affects also modal parameters – frequency, mode shape and damping (Maia et al (1997)). A lot of research is focused on high precision of processing methods and high added value, for example damage localisation. Katunin et al (2015) in their laboratory measurements show satisfactory reliability of vibration-based SHM approaches for damage localization. Promising approaches are presented in Janeliukstis et al (2020), where defect localisation is successfully utilized for a composite plate in a laboratory environment. Although it is advantageous to have full information on structural state, still laboratory tested approaches have difficulties in application to a real-life situation, which includes small differences in structures of the same type and series, significant variability in operation conditions (loads, external forces, temperature) and other unpredictable factors. Operational modal analysis OMA (Brincker and Ventura (2015)) is a branch of conventional Modal Analysis. It comes from the name that OMA measurements are performed during structures operation. This is very advantageous, as OMA potentially enables SHM without disrupting operations, eliminating downtime, increasing efficiency of operations. OMA approach would be a desirable choice for structural health monitoring of structures under natural excitation conditions, like wind turbines or helicopter blades. However, structures like these experience variable operating conditions, as mentioned earlier, which have to be taken into account when analysing modal parameters. One other issue that operators face in structural health monitoring is the huge amount of data (vibrational responses, records of loads, rotating speed, temperature, etc.), especially for a series of typical structures. Human resources are necessary to analyse how modal parameters change for a specific structure, taking into account different operating conditions. This analysis can be substituted by an automated process. This paper introduces application of machine learning technique called anomaly detection (Zimek and Schubert (2017)) for analysis of modal parameters. The aim of the analysis is to detect sets of modal parameters which represent an anomaly compared to the majority, which is an indication of damage. The proposed method establishes a description of normality using features representing undamaged conditions and then test for abnormality which indicates presence of damage in structure. 2. Theoretical considerations 2.1. Modal parameters estimation Operational modal analysis is based on the experimental modal analysis (EMA) theory with slight modification. In EMA the vibrational output response can be transformed into cross power spectra matrix between degrees-of-freedom: ( ) = ( ) ( ) ( ) . (1) where ( ) is the matrix of frequency response functions and ( ) is the force energy matrix, and ( ) is the Hermitian transform of the FRF matrix. The response is usually measured using accelerometers or strain gauges. Recent developments by Mironov et al (2020) show that it is equally possible to use industrial piezo film sensors which measure dynamic signals proportional to deformation velocity. The forces are obtained from load cells of other force gauges (for example impact hammers). In OMA, however, force is not measured, but assumed to be Gaussian in nature and evenly distributed across the excited system, i.e., force is assumed to be white noise. Thus, vibrational responses are assumed to be systems FRFs: ( ) ∝ ( ) ( ) . (2) Power spectra matrix is then decomposed at each frequency using Singular Value Decomposition (SVD), ( ) = ( ) ( ) ( ) , (3)