Domain Contextual and Relational Graph Model for Predictive Maintenance

The rotating machine is one of the common components which is examined under predictive maintenance across different industry sectors. Failure of bearings and gear boxes in such rotating machines are typical problems and their inspection is done using multiple sensors such as vibration, ultrasound, torque and temperature. The existing machine learning (ML) methods for bearing fault detection include traditional ML approaches and advanced deep learning algorithms. Nevertheless, these approaches often fail to account for the domain context and relationships between features, which are essential for building generalized, knowledge oriented, and trustworthy models. This work attempts to incorporate these aspects using a domain contextual and relational graph model (DCRG). It involves a graph convolutional network which is constructed with domain inspired features and their relations based on domain knowledge. The proposed method has been inspected using open-source bearing datasets such as Franche-Comté Électronique Mécanique Thermique et Optique (FEMTO), and Xi'an Jiaotong University (XJTU) bearing dataset. DCRG achieves stronger F1 scores (improvement by 5–10%) than the baseline machine learning approaches.