Graph Embedding-Based Bayesian Network for Fault Isolation in Complex Equipment

Abstract

Fault isolation, or fault location, aims to identify anomalous components at the start of the maintenance process. However, fault isolation within complex equipment can be challenging due to constraints on the scarcity of labeled data and the intricate interaction among various substructures. To overcome this challenge, an embedding-based Bayesian Network (BN) probability inference is proposed to locate the fault components, where the embedding, derived from semantic meanings, can approximate the actual fault distribution within BN. First, a Fault Graph (FG) is established based on the equipment's mechanical structure and its mechanisms. Then, a Multifield hyperbolic embedding is employed to vectorize the nodes in the FG, thereby preserving the inherent logic maximally. Following this, the FG is transformed into the BN, which facilitates the prediction of the faulty component based on available evidence, using the well-trained graph embedding. An empirical study on oil drilling equipment showcases the graph embedding properties and inference performance of the proposed method by comparing it with other cutting-edge methods and traditional scenarios.