Sensors Faults Classification and Faulty Signals Reconstruction Using Deep Learning

Sensor fault classification and reconstruction frameworks are crucial for the stable, safe, and reliable operations of Structural Health Monitoring (SHM) systems. Existing literature addressing reliability and efficiency is confronted with several challenges; especially, lacking a combined framework addressing both issues of classification and reconstruction at the same time. To tackle these issues, this paper proposes a fault-tolerant mechanism that uses various combinations of Deep Learning (DL) techniques to ensure the effectiveness and reliability of SHM systems in a resource-efficient way. The proposed mechanism is an integrated framework consisting of two modules: the sensor faults classification module and the faulty signal reconstruction module. We develop integrated architectures of CNN and RNN to classify faulty signals and employ various architectures of LSTM models for faulty signal reconstruction. Both modules are tested on the benchmark Canton Tower dataset. We augment the dataset with faulty signals created through simulations for an accurate analysis. The sensor faults classification module is evaluated by utilizing precision, recall, F1-score, and accuracy; it achieves a maximum accuracy of 94%. Additionally, the root mean square error (RMSE) value for the faulty signals' reconstruction stands at zero. The experimental results show that our proposed mechanism outperforms existing state-of-the-art techniques regarding sensor fault classification accuracy and the quality of reconstructed faulty signals.