Fault detection and classification in Industrial IoT in case of missing sensor data

Abstract

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

Information Processing and Data Visualization in Networked Industrial Systems

“…1). In fact, as detailed in [14], we propose to validate the GAN performance by assessing the impact of the generated data on the fault detection and classification modules. This way, the GAN hyperparameter optimization is driven by the resulting repercussions of using imputed data on the industrial monitoring system.…”

“…It is worth noting that while in [14] the fault detection and classification modules are implemented as an autoencoder and a deep neural network respectively, other options are viable and can be integrated with the GAN. The fault detection module can be implemented through any anomaly detection technique (e.g., clustering, autoencoder, principal component analysis).…”