Adaptive Transfer Learning Based Anomaly Detection for Industrial IoT System

Abstract

This paper proposes a transfer learning-based anomaly detection framework coupled with adaptive learning for predictive maintenance in Industrial Internet of Things (IIoT) systems. The proposed framework is designed and validated using a small-scale prototype to demonstrate its feasibility and effectiveness. The proposed architecture addresses the computational limitations of edge devices by employing a cloud-assisted/remote processing framework, where sensor data are streamed through Apache Kafka for real-time analysis. A lightweight Conditional Autoencoder is used to detect anomalies from streaming sensor data, making the framework suitable for resource-constrained industrial environments. To overcome the scarcity of machinespecific training data, a global model is trained using data collected from machines belonging to the same machine category. The trained global model is subsequently adapted to individual machines using transfer learning whenever machine-specific data are available. The framework continuously monitors incoming sensor data for concept drift and performs adaptive retraining when persistent drift is detected, enabling the model to learn changes in machine behavior caused by aging and wear. The system processes incoming data through parallel anomaly detection and drift detection pipelines while storing labeled operational data for future model adaptation and retraining. The proposed framework provides an adaptive and scalable approach for real-time anomaly detection in Industrial IoT predictive maintenance systems. The experimental implementation is carried out using a remote processing framework. The proposed architecture is cloud-ready and is intended to support cloudassisted deployment for large-scale Industrial IoT environments in future work.