On the Effectiveness of Dimensionality Reduction for Unsupervised Structural Health Monitoring Anomaly Detection

dc.contributor.authorSoleimani-Babakamali, Mohammad Hesamen
dc.contributor.committeechairLourentzou, Isminien
dc.contributor.committeememberFraghally, Mohammed Fawzi Seddiken
dc.contributor.committeememberSarlo, Rodrigoen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2022-05-23T14:29:31Zen
dc.date.available2022-05-23T14:29:31Zen
dc.date.issued2022-04-19en
dc.description.abstractDimensionality reduction techniques (DR) enhance data interpretability and reduce space complexity, though at the cost of information loss. Such methods have been prevalent in the Structural Health Monitoring (SHM) anomaly detection literature. While DR is favorable in supervised anomaly detection, where possible novelties are known a priori, the efficacy is less clear in unsupervised detection. In this work, we perform a detailed assessment of the DR performance trade-offs to determine whether the information loss imposed by DR can impact SHM performance for previously unseen novelties. As a basis for our analysis, we rely on an SHM anomaly detection method operating on input signals' fast Fourier transform (FFT). FFT is regarded as a raw, frequency-domain feature that allows studying various DR techniques. We design extensive experiments comparing various DR techniques, including neural autoencoder models, to capture the impact on two SHM benchmark datasets exclusively. Results imply the loss of information to be more detrimental, reducing the novelty detection accuracy by up to 60\% with autoencoder-based DR. Regularization can alleviate some of the challenges though unpredictable. Dimensions of substantial vibrational information mostly survive DR; thus, the regularization impact suggests that these dimensions are not reliable damage-sensitive features regarding unseen faults. Consequently, we argue that designing new SHM anomaly detection methods that can work with high-dimensional raw features is a necessary research direction and present open challenges and future directions.en
dc.description.abstractgeneralStructural health monitoring (SHM) aids the timely maintenance of infrastructures, saving human lives and natural resources. Infrastructure will undergo unseen damages in the future. Thus, data-driven SHM techniques for handling unlabeled data (i.e., unsupervised learning) are suitable for real-world usage. Lacking labels and defined data classes, data instances are categorized through similarities, i.e., distances. Still, distance metrics in high-dimensional spaces can become meaningless. As a result, applying methods to reduce data dimensions is currently practiced, yet, at the cost of information loss. Naturally, a trade-off exists between the loss of information and the increased interpretability of low-dimensional spaces induced by dimensionality reduction procedures. This study proposes an unsupervised SHM technique that works with low and high-dimensional data to assess that trade-off. Results show the negative impacts of dimensionality reduction to be more severe than its benefits. Developing unsupervised SHM methods with raw data is thus encouraged for real-world applications.en
dc.description.degreeM.S.en
dc.format.mediumETDen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/110138en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectUnsupervised SHMen
dc.subjectGenerative Adversarial Networksen
dc.subjectAnomaly Detectionen
dc.subjectDimensionality Reductionen
dc.subjectAutoencoderen
dc.subjectRegularizationen
dc.titleOn the Effectiveness of Dimensionality Reduction for Unsupervised Structural Health Monitoring Anomaly Detectionen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameM.S.en

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