Vibration‐based structural health monitoring exploiting a combination of convolutional neural networks and autoencoders for temperature effects neutralization

Abstract

Damage diagnosis in the structural field (mechanical, civil, aerospace, etc.) is a topic of active development and research. In recent years, considerable enhancements in this field have been achieved mainly due to advances in sensor technologies, the evolution of signal processing algorithms, and the increase of computational power. As one of the main consequences, the amount of data recorded from the sensorial equipment has steadily grown in quantity and complexity. In addition to that, these data are almost always significantly affected by many factors, which are not only related to the presence of damages but, for instance, also to the environmental and operative conditions under which the structural system is working. In order to handle these challenges, in the last few years, new deep learning models have been proposed, based on deep and heterogeneous architectures, able to deal with big data, also containing intricate diagnostic features that are difficult to be extracted. With this aim, this paper proposes a new vibration-based structural diagnosis tool that exploits the power of convolutional neural networks (CNNs) to extract subtle damage-related features from complex transmissibility function (TF) spectra even in presence of potentially confounding temperature variations. The diagnostic algorithm stems from the coupling of a CNN with an unsupervised anomaly detection algorithm based on autoencoders (AEs) to neutralize the effects of temperature variations and increase the damage diagnosis accuracy. The proposed approach is demonstrated with reference to a simple, but realistic, numerical case study of a structural beam subjected to temperature changes.

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

“…To effectively extract damage-sensitive features from vibration signals, it is necessary to adopt methods to process data from time and space dimensions. The problem of data temporal and spatial feature extraction can be solved using a combination of the CNN and RNN methods, such as CNN with GRU [64,109,123], CNN with LSTM [27,[178][179][180][181][182], CNN with Auto-encoder and [183], CNN with echo state networks [113], etc.…”

“…Dang et al [178] combined CNN with LSTM to process vibration signals, and they achieved high-accuracy bridge DD with reduced time and memory complexity. Parziale et al [183] proposed a coupling method of a CNN and autoencoder to neutralize the influence of temperature change and improve the accuracy of damage detection on a limited dataset.…”

Anomaly detection in Structural Health Monitoring using spectral distance and t-SNE–GMM framework under ambient excitation

“…The study addresses the challenge of incorporating noisy data from lower quality accelerometers and proposes techniques for classification while accounting for noise levels. Marc et al [34] applied convolutional neural networks (CNNs) to extract subtle damage-related features from complex transmissibility function (TF) spectra, even in the presence of potentially confounding temperature variations. The integration of unsupervised anomaly detection algorithm based on autoencoders (AEs) further enhances the accuracy of damage diagnosis by neutralizing the effect of temperature variations.…”

Physics-Informed Neural Networks for the Condition Monitoring of Rotating Shafts

“…However, the availability of big data is itself a new layer of complexity, especially in the realm of signal processing. That is, the rapid expansion of the amount of acquired data has introduced the need for (i) improved hardware and software performance, and (ii) developing tools to deal with confounding factors, including those unrelated to the system health state, such as environmental and operational conditions [ 10 , 11 ].…”