Semi-Bipartite Graph-Based Representation Learning Method for Remaining Useful Life Prognosis With Missing Values

Remaining useful life (RUL) prediction plays a pivotal role in prognostics and health management (PHM) systems, which enhances the reliability of operating equipment and reduces maintenance costs. With the advent of Industrial Internet of Things (IIoT) technology, it becomes feasible to obtain performance-degradation data that precisely mirrors the health status of equipment. This facilitates real-time process monitoring of device status and promotes intelligent predictive maintenance methods, thereby achieving more accurate RUL estimations. Nonetheless, IIoT systems often suffer from sensing or communication failures in practical industrial scenarios, leading to fragmented multivariate time-series data with missing observations from partial edge devices, which severely restricts the performance of RUL prediction. To address this issue, a graph-based representation learning (RL) method is skillfully proposed for RUL prediction with missing values. Specifically, the observations and features in multivariate time-series are regarded as two distinct nodes types in a semi-bipartite graph. Furthermore, the observed values are viewed as real edges whereas the dependency between timestamps and the correlation between features as virtual edges. In this scheme, the RL for multivariate time-series is expressed as the graph-level tasks. The acquired representations in the observation missing mode possess the capability to extract meaningful information and unveil underlying patterns from fragmented data, which bolsters the performance of RUL estimations through direct imputation or end-to-end architecture. The effectiveness and robustness of the proposed method are validated through the results of comparative experiments under different missing patterns using the C-MAPSS dataset, demonstrating its capability to enhance predictive maintenance in IIoT-enabled systems while reducing unexpected failures and maintenance costs.