基于贝叶斯网络的测井车载数据中心设备故障诊断方法研究

针对测井车载数据中心设备提出一种基于贝叶斯网络（BN）的故障诊断方法，并建立了故障诊断BN模型，实现了测井车载数据中心设备的智能化诊断。在建立BN模型过程中，采用基于粗糙集的知识约简方法对专家提出的故障节点进行简化，利用专家知识初步构建BN结构，使用K2学习算法对BN进行优化；同时结合专家经验知识，用EM算法对BN进行参数学习，得到测井车载数据中心设备故障诊断BN模型。运用BN模型进行故障案例推理，基于历史数据对BN模型进行验证，结果表明该模型诊断准确率较高，能够为测井车载数据中心设备故障的快速定位和精确诊断提供依据。

Fault Diagnosis Method of Logging Onboard Data Center Equipment Based on Bayesian Network

The fault diagnosis method based on Bayesian network (BN) is proposed for the logging onboard data center equipment. Meanwhile, the BN model for fault diagnosis is established to allow the logging onboard data center equipment to came under intelligent diagnosis. In the process of building the BN model, the knowledge reduced and simplified method based on the rough set is adopted to simplify the failure nodes put forward by experts. The BN structure is initially constructed on the basis of experts’ knowledge, with the K2 learning algorithm used for BN optimization. Combined with the experience and knowledge of experts, the EM algorithm is used to learn the parameters for BN, thus acquiring the fault -diagnosed BN model of the logging onboard data center equipment. The BN model is used to diagnose the fault cases while the historical data are based to verify the BN model. The results indicates that the model has a higher correction rate of diagnosis and the ability to provide the basis for rapidly positioning and precisely diagnosing the fault of the logging onboard data center equipment.