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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.

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This paper presents a deep learning approach for detecting early fault in bearings. The identification of bearings defects represents an important problem in the field of rotating machines. Sudden failures may occur, leading to breakdown of the machinery. For this reason, the prediction of possible faults has become a major issue in the study of bearing elements. Different fault diagnosis techniques have been developed during the years based on aggregated parameters (i.e. features) that are computed starting from time domain, frequency domain or time frequency domain analysis, relying on prior knowledge about signal processing. These approaches present major limitations, that can be overcome by adopting a convolutional LSTM (long short-term memory) neural network model. In this case, a more complex architecture is built, and the algorithm can identify effective features from accelerometer signal, that could not be considered by a manual computation approach. The algorithm has been applied on data obtained from a complex test rig to assess bearings failure on high speed trains. The outcome of this work indicates that the adopted approach leads to satisfactory performances.