Semi-Supervised Anomaly Detection in the TinyML Domain Through Multi-Target Few-Shot Domain Adaptation

TinyML, defined by its ability to integrate AI into the smallest devices, unlocks the potential for always-on AI solutions at the edge. This capability is essential in predictive maintenance, enabling smart sensors within the Industrial In ternet of Things (IIoT). In factory automation, the ability to detect anomalies and adapt those anomaly detection models to varying operating conditions is crucial for the efficiency and longevity of machinery. However, the challenge of semi supervised anomaly detection with domain adaptation across diverse working conditions has not been sufficiently addressed in TinyML. This paper presents Shared Encoder Domain Adaptation (SEDA) to overcome these limitations. SEDA is a multi-target domain adaptation method for anomaly detection tailored to TinyML applications. Our approach facilitates the transfer of knowledge from existing domains to new ones, ensuring reliable anomaly detection in all domains with minimal data under new operational conditions. This is particularly relevant for IIoT applications, where sensors must perform condition monitoring in a self-learning manner. The effectiveness of our method is evaluated through an extensive parameter study on the Toy ADMOS 2 data set and compared to various baseline methods. The results show an improvement of up to 0.212 on the Area Under the Receiver Operating Characteristic Curve (ROC-AUC) score. It demonstrates that our method significantly enhances performance in multi-target domain adaptation. Importantly, it does this while remaining efficient for resource-constrained systems. This method has the potential to enable self-learning sensor systems with adaptation strategies to different working conditions while requiring only a few data points from these new domains.