A Deep Learning Approach to Recover High-g Shock Signals from the Faulty Accelerometer

A deep learning based approach is proposed to accurately recover shock signals measured from a damaged high-g accelerometer without modifying the hardware. We first conducted shock tests and collected a large dataset of shock signals with different levels of acceleration by using an efficient experimental apparatus. The training data is composed of a pair of signals simultaneously obtained from a faulty accelerometer and a high-end accelerometer (served as the ground truth). A customized autoencoder neural network is designed and trained on this dataset, aiming to map the faulty signals to their reference counterparts. Experimental results show that, with the help of deep learning, shock signals can be accurately recovered from the faulty measurements. Compared with conventional approaches that require diagnosing and replacing faulty parts, the proposed data-driven method demonstrates a highly promising solution that allows recovering corrupted signals without introducing extra work to upgrade the hardware at almost zero cost. The dataset and code of this work are made publicly available on GitHub at https://github.com/hope-yao/Sensor_Calibration.