Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring

Abstract

Artificial Intelligence applied to Structural Health Monitoring (SHM) has provided considerable advantages in the accuracy and quality of the estimated structural integrity. Nevertheless, several challenges still need to be tackled in the SHM field, which extended the monitoring process beyond the mere data analytics and structural assessment task. Besides, one of the open problems in the field relates to the communication layer of the sensor networks since the continuous collection of long time series from multiple sensing units rapidly consumes the available memory resources, and requires complicated protocol to avoid network congestion. In this scenario, the present work presents a comprehensive framework for vibration-based diagnostics, in which data compression techniques are firstly introduced as a means to shrink the dimension of the data to be managed through the system. Then, neural network models solving binary classification problems were implemented for the sake of damage detection, also encompassing the influence of environmental factors in the evaluation of the structural status. Moreover, the potential degradation induced by the usage of low cost sensors on the adopted framework was evaluated: Additional analyses were performed in which experimental data were corrupted with the noise characterizing MEMS sensors. The proposed solutions were tested with experimental data from the Z24 bridge use case, proving that the amalgam of data compression, optimized (i.e., low complexity) machine learning architectures and environmental information allows to attain high classification scores, i.e., accuracy and precision greater than 96% and 95%, respectively.

Comparison of Common Algorithms for Single-Pixel Imaging via Compressed Sensing

“…(3) Machine learning is adopted to optimize the measurement matrix and reconstruction algorithm of CSSPI at the same time to achieve the best match, which is expected to strike a balance between measurement efficiency and imaging quality. There have been some efforts to use machine learning algorithms to improve the performance of CS, which shows the strong impact of machine learning on CS [ 87 , 171 , 172 , 173 , 174 ].…”

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

“…Stochastic Subspace Identification (SSI) is a param method that fits a parametric model directly into the ra a parametric model is considered as a mathematical m be changed to alter how the model fits to the data [34] ologies of SSI are data-driven (SSI-data) and covari pointed out that SSI-data methods are less straightforw pensive than the SSI-cov methods [36,37].…”

Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach

“…Compressive sensing (CS) has been a growing research topic [3][4][5] providing advances in various fields, including medicine [6], radar [7], and signal and image processing [8][9][10][11]. The CS-based methods allow signal reconstruction from a small subset of samples selected to favor a specific signal feature [12][13][14][15][16].…”