CNN and Convolutional Autoencoder (CAE) based real-time sensor fault detection, localization, and correction

Abstract

Increasing advances in sensing technologies and analytics have led to the proliferation of sensors to monitor structural and infrastructural systems. Accurate sensor data can provide information about structural health, aid in prognosis, and help calculate forces for vibration control. However, sensors are susceptible to faults such as loss of data, random noise, bias, drift, etc., due to the aging of sensors, defects, or environmental factors. Although traditional signal processing techniques can detect and isolate faults and reconstruct corrupt or missing sensor data, they demand significant human intervention. The continuous rise in computational power and demonstrated efficacy in numerous domains motivates the use of deep learning to minimize human-in-the-loop techniques. In this work, we introduce a novel, deep learning framework for linear systems with time-invariant parameters that identifies the presence and type of fault in sensor data, location of the faulty sensor and subsequently reconstructs the correct sensor data for fault detection, fault classification, and reconstruction. In our framework, first, a Convolutional Neural Network (CNN) is used to detect the presence of a fault and identify its type. Next, a suite of individually trained Convolutional Autoencoder (CAE) networks corresponding to each type of fault are employed for reconstruction. We demonstrate the efficacy of our framework to address both single and multiple sensor faults in synthetically generated data of a simple shear-type structure and experimentally measured data from a simplified arch bridge. While the framework is agnostic of fault-type, we demonstrate its use for four types of fault namely, missing, spiky, random, and drift. For both simulated and experimental datasets with a single fault, our models performed well, achieving 100% accuracy in faulty sensor localization, more than 98.7% accuracy in fault type detection, and more than 99% accuracy in reconstruction. Our framework can also address multiple concurrent faults with similar accuracy. We empirically demonstrate that our proposed framework performs better than other state-of-the-art techniques in terms of computational efficiency with comparable accuracy. Adoption of our framework in online structural health monitoring applications can lead to minimal disruption to monitoring processes, reduced downtime for structures and infrastructure while simultaneously reducing uncertainty and improving the quality of sensor data for historical records.