A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features

This study proposes a novelty-classification framework that applies to structural health monitoring (SHM) and sensor output validation (SOV) problems. The proposed framework has simple high-dimensional features with several advantages. First, the feature extraction method is extensively applicable to instrumented structures. Second, the high-dimensional features’ utilization alleviates one of the main issues of supervised novelty classifications, namely, imbalanced datasets and low-sampled data classes. Recurrent Neural Networks are employed for the classification of high-dimensional features. Furthermore, generative adversarial networks (GAN) are trained with low-sampled data classes’ high-dimensional features for generating new data objects. The generated data objects are combined with the initial training set for improving classification results. The proposed framework is studied on two SHM and SOV datasets. The SHM dataset has twenty-one data classes, with a total test accuracy of 99.60% compared to another study with 88.13% accuracy. The SOV classification shows improved results with a mean accuracy of 96.5% compared to three other studies with mean accuracy values of 93.5%, 92.97%, and 71.1%. Furthermore, the integration of GAN’s generated data objects with low-sampled classes improved those classes’ mean F1 score from 44.77% to 64.58% and from 73.39% to 90.84% on SOV and SHM case studies, respectively. The integration of GAN-generated data objects with the initial low-sampled data classes for accuracy improvement shows more potential in the SHM dataset than the SOV case, which can be due to the signal pattern-based labeling logic of SOV datasets.