Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things

TL;DR

This paper introduces a federated learning approach using autoencoders for condition monitoring in industrial IoT, enabling privacy-preserving, distributed training on edge devices with competitive performance and reduced resource use.

Contribution

It presents a novel federated autoencoder-based method for industrial condition monitoring that preserves data privacy and reduces network load, unlike traditional centralized approaches.

Findings

Achieved comparable detection accuracy to centralized models.

Significantly reduced network bandwidth and resource consumption.

Validated on real-world datasets and multiple testbeds.

Abstract

Enabled by the increasing availability of sensor data monitored from production machinery, condition monitoring and predictive maintenance methods are key pillars for an efficient and robust manufacturing production cycle in the Industrial Internet of Things. The employment of machine learning models to detect and predict deteriorating behavior by analyzing a variety of data collected across several industrial environments shows promising results in recent works, yet also often requires transferring the sensor data to centralized servers located in the cloud. Moreover, although collaborating and sharing knowledge between industry sites yields large benefits, especially in the area of condition monitoring, it is often prohibited due to data privacy issues. To tackle this situation, we propose an Autoencoder-based Federated Learning method utilizing vibration sensor data from rotating machines, that allows for a distributed training on edge devices, located on-premise and close to the monitored machines. Preserving data privacy and at the same time exonerating possibly unreliable network connections of remote sites, our approach enables knowledge transfer across organizational boundaries, without sharing the monitored data. We conducted an evaluation utilizing two real-world datasets as well as multiple testbeds and the results indicate that our method enables a competitive performance compared to previous results, while significantly reducing the resource and network utilization.