基于知识图谱和多任务学习的工业生产关键设备故障诊断方法

Fault diagnosis method of critical industrial equipment based on knowledge graphs and multi-task learning

针对工业生产关键设备故障数据稀疏的问题和故障诊断的需求,本文提出了一种基于知识图谱和多任务学习的工业生产关键设备故障诊断模型MKFD (multi-task learning for knowledge graphenhanced fault diagnosis),通过对故障根因的推断实现故障诊断.设计了多任务学习框架,并构造了一种改进十字绣单元用于实现框架内子任务之间的信息共享.利用运维数据构建故障现象–故障根因关联矩阵,使用多层感知机搭建知识图谱嵌入模型;分别将关联矩阵嵌入和知识图谱嵌入作为多任务学习框架中的两个子任务,通过子任务的交替学习,优化MKFD模型参数,实现对故障根因的推断,从而达到故障诊断的目的.最后,基于国内某工业企业的运维数据所构建的两个工业生产关键设备故障知识图谱对上述方案进行了验证实验,结果证明所提出的方法具有良好的性能.

To resolve the problem of sparse fault data in critical industrial equipment and meet the demands of fault diagnosis,we propose a critical industrial equipment fault diagnosis model,termed multi-task learning for knowledge graph-enhanced fault diagnosis(MKFD),which is based on a knowledge graph and multi-task learning.The model realizes the inference of fault root causes.First,a multi-task learning framework is designed,and an improved cross-stitch network is constructed to realize the information sharing among subtasks in the framework.Then,the interaction matrix of fault phenomena and fault root causes is constructed using operation and maintenance data,and the knowledge graph embedding model is built using a multi-layer perceptron.The embedding of the interaction matrix and knowledge graph is regarded as two subtasks in the multi-task learning framework.Through the alternative learning of subtasks,the parameters of MKFD are optimized to infer the fault root cause,thus assisting the operation and maintenance personnel in fault diagnosis.Moreover,this scheme was verified by constructing two critical industrial equipment fault knowledge graphs based on the operation and maintenance data of a domestic industrial enterprise,and the results show that the proposed method has good performance.