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Item type:Publication, Access status: without files , Fault Diagnosis in Structural Health Monitoring Systems Using Signal Processing and Machine Learning TechniquesSmart structures leverage intelligent structural health monitoring (SHM) systems, which comprise sensors and processing units deployed to transform sensor data into decisions. Faulty sensors may compromise the reliability of SHM systems, causing data corruption, data loss, and erroneous judgment of structural conditions. Fault diagnosis (FD) of SHM systems encompasses the detection, isolation, identification, and accommodation of sensor faults, aiming to ensure the reliability of SHM systems. Typically, FD is based on “analytical redundancy,” utilizing correlated sensor data inherent to the SHM system. However, most analytical redundancy FD approaches neglect the fault identification step and are tailored to specific types of sensor data. In this chapter, an analytical redundancy FD approach for SHM systems is presented, coupling methods for processing any type of sensor data and two machine learning (ML) techniques, (i) an ML regression algorithm used for fault detection, fault isolation, and fault accommodation, and (ii) an ML classification algorithm used for fault identification. The FD approach is validated using an artificial neural network as ML regression algorithm and a convolutional neural network as ML classification algorithm. Validation is performed through a real-world SHM system in operation at a railway bridge. The results demonstrate the suitability of the FD approach for ensuring reliable SHM systems.Publicationtype: Journal Article183 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A support vector regression-based approach towards decentralized fault diagnosis in wireless structural health monitoring systems(2019-10); ; The reliability of sensors in structural health monitoring (SHM) systems is affected by sensor faults that compromise the quality of monitoring, causing erroneous judgment of structural conditions. To ensure reliable operation of SHM systems, techniques for diagnosing sensor faults have been proposed. In wireless SHM systems, embedding techniques for fault diagnosis (FD) into sensor nodes is of increasing importance as sensor nodes process data on board and communicate analysis results instead of large sets of raw data. As a consequence of on-board processing, raw data is frequently unavailable and sensor faults remain undetected. In this paper, a decentralized fault diagnosis approach based on support vector regression (FD-SVR) is proposed. Due to the high accuracy of the support vector regression (SVR), which can be achieved even with relatively small data sets, the FD-SVR approach enables wireless sensor nodes autonomously self-diagnose sensor faults, enhancing the reliability of wireless SHM systems without a need for large data sets to be used for fault diagnosis. The ability of the embedded FD-SVR approach to detect and isolate sensor faults, increasing the reliability of sensors in wireless SHM systems, is validated in laboratory experiments.Publicationtype: Conference Paper43 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Monitoringgestützte Analyse von Schwingungsdämpfern in Turmbauwerken(2018-06); ; ; Monitoring-based performance analysis of tuned mass dampers in tower structures. Slender structures, such as towers and masts, frequently exhibit unfavourable responses when subjected to dynamic loads, characterized by the non-negligible contribution of higher modes of vibration as well as resonant phenomena between the loads and the structure. To mitigate such responses, tuned mass dampers (TMDs) have been employed in several slender structures. A TMD is a structural sub-system fixed to a structure and “tuned” to counteract a response frequency, which is anticipated to induce large oscillations to the structure, through its out-of-phase response. The design of tuned mass dampers is based on modelling assumptions and on sparse observations of the dynamic behaviour of structures, which changes due to structural ageing, thus rendering the regular performance assessment of TMDs is essential. In this paper, a methodology for assessing TMD performance using structural health monitoring (SHM) data is presented. Specifically, the TMD functionality is assessed upon obtaining the phase shift between the TMD response and the response of the structure, which, according to TMD theory, indicates favourable dampening effect when assuming values close to 90°. The proposed methodology is validated via a case study of a telecommunication tower equipped with a TMD and an SHM system, showcasing the ability of the methodology to assess TMD performance.Publicationtype: Journal Article44