基于优化分簇贝叶斯网的转子振动故障诊断

针对柴油发动机的充电发电机结构及振动的复杂性导致其转子振动故障具有多层次性、耦合性和随机性,以及故障信息不完整性等特点，提出了一种基于振动频谱分析和贝叶斯网络的转子振动故障诊断方法。该方法将故障源和故障现象根据专家经验数值化表示并离散化，运用改进的优化分簇算法，构建特定振动故障类型的贝叶斯诊断网络，利用贝叶斯网络推理算法诊断出故障概率分布，并利用具体的故障证据、设定值对该方法进行验证。仿真及实验结果表明,该方法能在故障信息不完整情况下，依据不完整证据信息更新各网络节点的概率状态，实现对不确定信息的推理和估计，得到较好的诊断结果,提高了转子振动故障的诊断准确度。

Generator Rotor Vibration Fault Diagnosis Based on Optimization Clustering Bayesian Network

In order to improve the diagnosic accuracy of the charging generator rotor vibration fault, the work presented in this paper focuses on using frequency spectrum analysis and Bayesian networks to diagnose the typical vibration fault of generator rotor vibration. The vibration fault has properties of randomnessand layers, and its information has the features of uncertainty and non-integrality. The method of typical rotor vibration fault diagnosis is studied based on Bayesian networks, which uses expert knowledge to determine conditional probability, turning fault information into numeric vectors. Then, the Bayesian network model of vibration fault diagnosis is established using spectrum data. The clustering algorithm is improved to minimite calculation and enhance diagnostic accuracy by optimizing connections between clustering nodes. Based on the condition that the known information is fuzzy and incomplete, the results of the experiment indicate that the Bayesian network-based method can improve the accuracy of diagnosing typical generator rotor vibration faults.