Transfer Learning from Audio Domains a Valuable Tool for Structural Health Monitoring

Abstract

Today, the application of artificial neural network tools to define models that mimic the dynamic behavior of structural systems is a wide-spread approach. A fundamental issue in developing these strategies for damage assessment in civil structures is represented by the unbalanced nature of the available databases, which commonly contain plenty of data coming from the structure under healthy operational conditions and very few samples from the system in unhealthy conditions since the structure would have failed by that time. Consequently, the learning task, carried on with standard deep learning approaches, becomes case-dependent and tends to be specialized for a particular structure and for a very limited number of damage scenarios. This work presents a framework for damage classification in structural systems intended to overcome such limitations. In this methodology, the model is trained to gain knowledge in the learning task from a rich acoustic dataset (source domain), acquiring higher-level features characterizing vibration traits from a rich acoustic dataset. This knowledge is then transferred to a target domain, with much less training data, such as a structural system, in order to classify its structural condition. The framework starts with constructing a time-delay neural network (TDNN) structure, trained on the VoxCeleb dataset, in the speech domain. The input of the network consists of Cepstral and pitch features extracted from the audio records. Higher-level features, the x-vectors, speaker embeddings, capturing neural outputs of the network’s intermediate layers, are derived and then used to train a probabilistic linear discriminant analysis (PLDA) model to provide a probabilistic discriminant model for speaker comparison. These features collect generic information regarding the source domain and characterize a classification process based on the frequency content of signals, which is not strictly dependent on the original acoustic domain. Because of the non-case-dependent nature of the x-vector embeddings (features), they can be used to train an alternative PLDA model to address a damage classification task, considering vibration measurements coming from a different system, a structural one which represents the target domain. The simulated data from the 12 degrees of freedom benchmark shear-building structure provided by the IASC-ASCE Structural Health Monitoring Group are studied to verify the proposed framework’s effectiveness.