基于贝叶斯网络的汽轮机组轴承工频振动诊断

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将贝叶斯网络方法用于汽轮机组轴承振动诊断。根据现场诊断经验,建立了轴承工频振动诊断的质朴型贝叶斯网络,网络中融合了振动频谱、相位和运行工况等诊断信息;提出了网络推理计算方法,并在LabVIEW软件平台上实现。诊断结果的准确性在多个实际案例中得到证实,表明该诊断方法能有效地识别轴承振动的单一故障和复合故障。 Bayesian network method was studied for vibration diagnosis of steam turbine bearings. According to practical engineering experience, a natural Bayesian network for working frequency vibration diagnosis of bearings was built. In the network, diagnostic information, such as, vibration frequency spectrum, phase, and operating conditions were all considered. The reasoning computation was completed by adopting LabVIEW platform. The diagnosis results were verified with serveral real cases. It was shown that the proposed method can be used to diagnose single and combined bearing vibration failures effectively.