Industrial Fault Diagnosis With Joint Graph Structure Optimization
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Solution Overview
Problem
Traditional fault diagnosis methods for industrial equipment face challenges such as low accuracy, poor real-time performance, and inefficiencies in large-scale data processing, particularly when using graph neural networks (GNNs), due to sparse graph structures, data noise, and limited computing resources, which hinder effective feature extraction and model transparency.
Innovation Solution
A method involving graph structure joint optimization using GCN and GAT networks, combined with a stochastic block model and Bayesian inference, to enhance graph structure learning and improve fault diagnosis accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNN is directly applied to fault diagnosis using original graph structures, then the model can process equipment data, but the sparse graph structures make it difficult to capture potential relationships between equipment and faults
Solution Approach 1:
The patent applies preliminary action by performing graph structure optimization before the main fault diagnosis task. The method pre-processes the original sparse graph by learning optimized graph structures that capture potential equipment-fault relationships more effectively, thereby improving subsequent diagnosis accuracy without dealing with the limitations of raw sparse data
Solution Approach 2:
The patent changes parameters by transforming the graph structure representation itself. Instead of using the original sparse adjacency matrix directly, the method learns optimized adjacency matrices that better represent the underlying equipment-fault relationships, effectively changing the structural parameters to improve relationship capture
2Measurement precision
If GNN algorithms process large volume of node data through message passing mechanism, then comprehensive feature aggregation is achieved, but training and inference speeds are significantly slowed down
Solution Approach 1:
The patent applies preliminary action by pre-learning optimized graph structures that encode important relationships more efficiently. This pre-processing step reduces the complexity of subsequent message passing operations, allowing the model to achieve comprehensive feature aggregation with fewer computational iterations, thereby improving training and inference speeds
Solution Approach 2:
The patent extracts and learns optimized graph structures that capture the most important equipment-fault relationships. By separating the graph structure learning from the standard message passing process, the method extracts essential structural information that can be used to guide or accelerate subsequent feature aggregation operations
3Ease of operation
If traditional fault diagnosis methods use rule-based approaches, then implementation is straightforward, but performance is poor when dealing with complex modes and changes of equipment faults
Solution Approach 1:
The patent replaces the mechanical rule-based system with a learned graph neural network model. Instead of manually defining rules for fault diagnosis, the method uses GNN to automatically learn diagnostic patterns from data, substituting rigid mechanical rule processing with flexible neural network-based reasoning that adapts to complex fault modes
4Productivity
If statistical models are used for fault diagnosis, then computation is efficient, but the models are insufficiently comprehensive in capturing in-depth operating states and fault modes
Solution Approach 1:
The patent creates a composite approach by combining graph neural networks with statistical methods. The GNN component captures complex hierarchical relationships and patterns in equipment data, while statistical methods provide efficient computation and baseline analysis, creating a composite system that achieves both comprehensiveness and efficiency
Data Source
AI summary
This disclosure relates to the technical field of fault diagnosis, in particular, to a method and system for industrial equipment fault diagnosis based on graph structure joint optimization. The method includes: acquiring an original equipment dataset; constructing an original graph structure based on the original equipment dataset; extracting two basic views based on the original graph structure, calculating graph node embeddings of the basic views using a GCN, and recalculating a probability of an edge in the graph structure based on the graph node embeddings; performing view fusion based on the probability of the edge in the graph structure to obtain a preliminarily optimized view; and processing a fused view through a GAT network to obtain an enhanced view. According to this disclosure, the problems of low prediction accuracy, poor robustness, the like in traditional fault diagnosis are optimized, and thus the stability of industrial Internet equipment is greatly improved.