基于动态图表示的设备故障推理预测方法

doi:10.11896/jsjkx.231000223

Abstract

Abstract: Effective equipment operation and maintenance is able to ensure the proper operation of equipment.Nevertheless,as the equipment becomes more and more sophisticated,the complexity and difficulty of maintaining and troubleshooting these devices are constantly increasing.As a result,equipment operation and maintenance mode that only rely on manual efforts is gradually unable to meet the requirements of intelligent equipment.Intelligent operation and maintenance that applies many new emerging technologies such as artificial intelligence to process of operation and maintenance can be used as a strong support for equipment operation and maintenance task.However,many existing methods still have deficiencies such as lack of considering dynamic cha-racteristics.In order to solve these problems,a device fault inference and prediction method that is based on dynamic knowledge graph representation learning is proposed.The method can predict whether a target device is potentially associated with a faulty device time during the operation and maintenance process.The proposed method combines dynamic knowledge graph representation learning with graph representation inference models,updates the graph network based on real-time data,and employs graph representation inference models to infer new fault data.Firstly,it takes advantage of a dynamic knowledge graph to represent the equipment operation and maintenance data,so as to records the evolution of the equipment over time.The representation effectively denote dynamic changes of the relationship between the devices.Next,the time-aware representations of the source faulty equipment and the target equipment in the dynamic knowledge graph are obtained through representation learning.Finally,the time-aware representations are used as inputs for fault inference prediction,which predicts whether there exists any potential correlation between the equipment so as to assist the operation and maintenance engineer in solving the corresponding equipment fault problems.Experiments on multiple datasets verify the effect of the proposed method.

Key words: Dynamic knowledge graph, Representation learning, Link inference prediction, Time awareness, Device operations and maintenance

CLC Number:

TP391

ZHANG Hui, ZHANG Xiaoxiong, DING Kun, LIU Shanshan. Device Fault Inference and Prediction Method Based on Dynamic Graph Representation[J].Computer Science, 2024, 51(7): 310-318.

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Device Fault Inference and Prediction Method Based on Dynamic Graph Representation

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