The impact of sensing parameters on data management and anomaly detection in structural health monitoring

The massive and autonomous structural health monitoring (SHM) of bridges is a problem that is of growing interest due to its importance and topicality. However, a considerable amount of data must be elaborated and managed in such an application. This paper proposes a set of machine learning (ML) tools to detect anomalies in a bridge from vibrational measurements using the minimum amount of data. The proposed framework starts from the fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by a density-based time-domain tracking algorithm. The funda- mental frequencies extracted are then fed to one-class classification (OCC) algorithms that perform anomaly detection. Then, to reduce the amount of data, we analyze the effect of the number of sensors, the number of bits per sample, the observation time, and the measurement noise on damage detection performance. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric measurements in both standard and damaged conditions. A comparison of OCC algorithms, such as principal component analysis (PCA), kernel principal component analysis (KPCA), Gaussian mixture model (GMM) and one-class classifier neural network (OCCNN)2 is performed, and their robustness to data shrinking is evaluated. In many cases, OCCNN2 increases the performance with respect to classical anomaly detection techniques in terms of accuracy

Multi-Base Station Cooperative Sensing with AI-Aided Tracking

In this work, we investigate the performance of a joint sensing and communication (JSC) network consisting of multiple base stations (BSs) that cooperate through a fusion center (FC) to exchange information about the sensed environment while concurrently establishing communication links with a set of user equipments (UEs). Each BS within the network operates as a monostatic radar system, enabling comprehensive scanning of the monitored area and generating range-angle maps that provide information regarding the position of a group of heterogeneous objects. The acquired maps are subsequently fused in the FC. Then, a convolutional neural network (CNN) is employed to infer the category of the targets, e.g., pedestrians or vehicles, and such information is exploited by an adaptive clustering algorithm to group the detections originating from the same target more effectively. Finally, two multi-target tracking algorithms, the probability hypothesis density (PHD) filter and multi-Bernoulli mixture (MBM) filter, are applied to estimate the state of the targets. Numerical results demonstrated that our framework could provide remarkable sensing performance, achieving an optimal sub-pattern assignment (OSPA) less than 60 cm, while keeping communication services to UEs with a reduction of the communication capacity in the order of 10% to 20%. The impact of the number of BSs engaged in sensing is also examined, and we show that in the specific case study, 3 BSs ensure a localization error below 1 m

Algoritmi di Machine Learning per il Riconoscimento Vocale

Con l’avvento dell’Internet of Things (IoT) la mole di dati prodotti da sensori e dispositivi connessi alla rete aumenterà in maniera esponenziale. Per gestire una tale mole di dati diventerà sempre più importante l’utilizzo di nuove strategie di elaborazione per classificare le informazioni raccolte, estrarre “feature” che caratterizzino un certo gruppo di dati, ed infine distillarne il contenuto (dimensionality reduction). In questa tesi si studiano e utilizzano alcuni algoritmi di Machine Learning adatti alla trattazione di problemi multidimensionali di classificazione e alla gestione di grandi quantità di dati (Big Data), in grado di estrarre feature per catalogare i dati. L’obiettivo è quindi quello di implementare alcuni algoritmi di Machine Learning per poi procedere ad un confronto delle prestazioni, sottoponendo vari tipi di problemi di classificazione agli algoritmi realizzati, al fine di verificare quale sia il miglior algoritmo da utilizzare data una certa tipologia di problema. La valutazione delle prestazioni verrà effettuata mediante metodi di misura dell'accuratezza della classificazione, come ad esempio la matrice di confusione, la probabilità di falso allarme (PF), e la probabilità di missed detection (PM)

Machine learning for automatic processing of modal analysis in damage detection of bridges

Autonomous structural health monitoring (SHM) of a large number of bridges became a topic of paramount importance for maintenance purposes and safety reasons. This article proposes a set of machine learning (ML) tools to perform automatic detection of anomalies in a bridge structure from vibrational data. As a case study, we considered the Z-24 bridge for which an extensive database of accelerometric data is available. The proposed framework starts from the stabilization diagram obtained through operational modal analysis (OMA) to perform the clustering of modal frequencies and their tracking by density-based time-domain filtering. The features extracted are then fed to a one-class classification (OCC) algorithm to perform anomaly detection. In particular, we propose two new anomaly detectors, namely, one-class classifier neural network (OCCNN) and OCCNN 2 , that find the normal class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimate. The detection algorithms are then compared with known methods based on the principal component analysis (PCA), the kernel PCA (KPCA), the Gaussian mixture model (GMM), and the autoassociative neural network (ANN). The proposed OCCNN solution presents increased accuracy and F 1 score over conventional algorithms, without the need to set critical parameters, while OCCNN 2 provides the best performance in terms of F 1 score, accuracy, and responsiveness

One Class Classifier Neural Network for Anomaly Detection in Low Dimensional Feature Spaces

In the last decade, many approaches have been developed to solve one-class classification (OCC) problems for anomaly detection. Many of them rely on estimating the statistical distribution of the data, find hidden patterns, or remap the data in advantageous feature spaces. This kind of techniques usually needs some a priori knowledge of the data distribution (i.e., Gaussian) or the setting of some parameters to achieve good classification performance, making their use less effective when the data distribution is unknown. In this paper, we propose a novel blind anomaly detection for low dimensional feature spaces, that exploits the flexibility of the neural network (NN) structure to find the class boundaries without any information about the shape of the data distribution. To prove the generality of the solution, we tested many different class shapes, and we applied it to a structural health monitoring (SHM) case study. Without requiring the tuning of hyperparameters, the performance of the proposed algorithm overcomes that of some known approaches like principal component analysis (PCA), kernel principal component analysis (KPCA), Gaussian mixture model (GMM), and autoassociative neural network (ANN) in many cases, and performs well in the specific SHM setting

Human activities classification using biaxial seismic sensors

In this letter, we propose a method for passive human activity classification exploiting ground vibrations observed by a biaxial geophone. The solution is grounded on the idea that some activities can be better analyzed by the horizontal channel (bicycle and car) and others by the vertical one (walk and run). Thus, the following two solutions are proposed: first, joint processing of the vertical and horizontal data by a single classifier and, second, cascade processing by two classifiers that analyze the two channels separately. Numerical results based on real data show that while a parametric method such as a support vector machine performs well in both cases, a nonparametric method such as the k-nearest neighbors reaches a higher accuracy in cascade processing. Besides, the results are compared with those obtained using a monoaxial geophone only