Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems

TL;DR

This paper introduces a hierarchical knowledge guided framework for fault intensity diagnosis in complex industrial systems, leveraging graph convolutional networks and hierarchical knowledge embedding to improve accuracy and robustness.

Contribution

The paper proposes a novel hierarchical knowledge guided fault diagnosis framework using graph convolutional networks and a re-weighted hierarchical knowledge correlation matrix, enhancing dependency modeling among classes.

Findings

Outperforms recent state-of-the-art FID methods on four real-world datasets.

Effectively captures class dependencies through hierarchical knowledge embedding.

Demonstrates robustness and superior accuracy in industrial fault diagnosis.

Abstract

Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems. As current FID methods are based on chain of thought without considering dependencies among target classes. To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods. The HKG uses graph convolutional networks to map the hierarchical topological graph of class representations into a set of interdependent global hierarchical classifiers, where each node is denoted by word embeddings of a class. These global hierarchical classifiers are applied to learned deep features extracted by representation learning, allowing the entire model to be end-to-end learnable. In addition, we develop a re-weighted hierarchical knowledge correlation matrix (Re-HKCM) scheme by embedding inter-class hierarchical knowledge into a data-driven statistical correlation matrix (SCM) which effectively guides the information sharing of nodes in graphical convolutional neural networks and avoids over-smoothing issues. The Re-HKCM is derived from the SCM through a series of mathematical transformations. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG and one existing publicly) for FID, all showing superior results and outperform recent state-of-the-art FID methods.