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.