This paper introduces a framework to perform operational modal analysis (OMA) for structural health monitoring (SHM) by presenting the development and validation of a low-power, solar-powered wireless sensor network (WSN) tailored for bridge structures. The system integrates accelerometers and temperature sensors for dynamic structural assessment, all interconnected through the energy-efficient message queuing telemetry transport (MQTT) messaging protocol. Moreover, it delves into the details of sensor selection, calibration, and the design considerations necessary to address the unique challenges associated with bridge structures. Special attention is given to the solar-powered aspect, allowing for extended deployment periods without the need for frequent maintenance or battery replacements. To validate the proposed system, a comprehensive field deployment was conducted on an actual bridge structure. The collected data were transmitted through MQTT messages and analyzed by means of OMA. Comparative studies with traditional wired systems underscore the advantages of the solar-powered wireless solution in terms of sustainability, scalability, and ease of deployment. Results from the validation phase demonstrate the system’s capability to provide accurate and real-time data needed to assess the health state of the monitored asset. This paper concludes with insights into the practical implications of adopting such a solar-powered WSN, emphasizing its potential to revolutionize bridge health monitoring by offering a cost-effective and energy-efficient solution for long-term infrastructure resilience.

In industrial settings, machinery components inevitably wear and degrade due to friction between moving parts. To address this, various maintenance strategies, including corrective, preventive, and predictive maintenance, are commonly employed. This paper focuses on predictive maintenance through vibration analysis, utilizing data-driven models. This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. By transforming vibration signals into images using the Synchrosqueezing Transform (SST), this research leverages the strengths of convolutional neural networks (CNNs) in image processing, which have proven effective in AD, especially at the pixel level. The methodology involves training CAEs and VAEs on data from machinery in healthy condition and testing them on new data samples representing different levels of system degradation. The results indicate that models with spatial latent spaces outperform those with dense latent spaces in terms of reconstruction accuracy and AD capabilities. However, VAEs did not yield satisfactory results, likely because reconstruction-based metrics are not entirely useful for AD purposes in such models. This study also highlights the potential of ReLU residuals in enhancing the visibility of anomalies. The data used in this study are openly available.

Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure’s health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge’s health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others’ $N-1$ measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness.