Proxy Task Anomaly Detection with Asset Administration Shell Integration for Predictive Maintenance

The core goal of Industry 4.0 is to build smarter, more adaptive, and interoperable manufacturing systems. Therefore, the concept of Asset Administration Shell (AAS) was proposed as a standardized interface and carrier for the digital expression of all objects that have value for an organization. Through years of development, AAS has become the standard for advanced development and application in the manufacturing industry. Predictive maintenance (PdM), as an important strategy in Industry 4.0, can detect early signs of failure through real-time data, optimizing equipment availability, reducing downtime and maintenance cost. However, current PdM implementations often suffer from data fragmentation, isolated modeling, limited generalizability, etc. Although AAS has great potential to effectively address these challenges, research in this area remains limited. This paper proposes a predictive maintenance method that integrates AAS and anomaly detection techniques. The method leverages the idea of proxy task by training a Long Short-Term Memory (LSTM) network on time-series sensor data and constructing anomaly scores using cosine distance metrics to effectively assess equipment conditions and provide predictive alerts based on it. In addition, the AAS models and corresponding submodels were designed and implemented with AASX Package Explorer, integrated with the PdM method using FA3ST tool. Finally, an industrial testbed was used as a practical case to verify the effectiveness and practicality of the proposed model.