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Browsing by Author "Soleimani-Babakamali, Mohammad Hesam"

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    On the Effectiveness of Dimensionality Reduction for Unsupervised Structural Health Monitoring Anomaly Detection
    Soleimani-Babakamali, Mohammad Hesam (Virginia Tech, 2022-04-19)
    Dimensionality 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.
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    Toward a General Novelty Detection Framework in Structural Health Monitoring; Challenges and Opportunities in Deep Learning
    Soleimani-Babakamali, Mohammad Hesam (Virginia Tech, 2022-10-17)
    Structural health monitoring (SHM) is an anomaly detection process. Data-driven SHM has gained much attention compared to the model-based strategy, specifically with the current state-of-the-art machine learning routines. Model-based methods require structural information, time-consuming model updating, and may fail with noisy data, a persistent condition in real-time SHM problems. However, there are several hindrances in supervised and unsupervised settings in machine learning-based SHM. This study identifies and addresses such hindrances with the versatility of state-of-the-art deep learning strategies. While managing those complications, we aim at proposing a general, structure-independent (ie requires no prior information) SHM framework. Developing such techniques plays a crucial role in the SHM of smart cities. In the supervised SHM and sensor output validation (SOV) category, data class imbalance results from the lack of data from nuanced structural states. Employing Long Short-Term Memory (LSTM) units, we developed a general technique that manages both SHM and SOV. The developed architecture accepts high-dimensional features, enabling the train of Generative Adversarial Networks for data generation, addressing the complications of data imbalance. GAN-generated SHM data improved accuracy for low-sampled classes from 44.77% to 64.58% and from 73.39% to 90.84% in two SOV and SHM case studies, respectively. Arguing the unsupervised SHM as a practical category since it identifies novelties (ie unseen states), the current application of dimensionality reduction (DR) in unsupervised SHM is investigated. Due to the curse of dimensionality, classical unsupervised techniques cannot function with high-dimensional features, driving the use of DR techniques. Investigations highlighted the importance of avoiding DR in unsupervised SHM, as data dimensions that DR suppresses may contain damage-sensitive features for novelties. With DR, novelty detection accuracy declined up to 60% in two benchmark SHM datasets. Other obstacles in the unsupervised SHM area are case-dependent features, lack of dynamic-class novelty detection, and the impact of user-defined detection parameters on novelty detection accuracy. We chose the fast Fourier transform-based (FFT) of raw signals with no dimensionality reduction to develop the SHM framework. A deep neural network scheme is developed to perform the pattern recognition of that high-dimensional data. The framework does not require prior information, with GAN models implemented, offering robustness to sensor placement in structures. These characteristics make the framework suitable for developing general unsupervised SHM techniques.