A Quality-Related Incipient Fault Detection and Location for Industrial IoT With Incomplete Data

In industrial internet of things (IIoT), sensor failures, network interruptions, and communication delays often lead to structured missing data, significantly hindering the timely detection of incipient faults. This paper addresses the problem of quality-related incipient fault detection and location (IFDL) in IIoT under incomplete data. Compared with existing methods that rely on fully observed data, IFDL integrates a neighborhood probability guided imputer with a multi-scale graph regularized autoencoder, preserving the temporal continuity and manifold structures that characterize incipient faults. With the restored data and learned features, GSVD-based correlated subspace modeling isolates the quality-related variations embedded in the process to ensure accurate identification of incipient faults. Furthermore, a confidence-weighted trajectory deviation index, combining contextual and Bayesian uncertainty, identifies the variables contributing to process faults. The effectiveness of the proposed method is demonstrated through both simulation studies and an industrial case.