往复式压缩机是石油化工生产的关键设备, 它的安全平稳运行与气阀的工作状态息息相关。为实现往复式压缩机气阀故障的快速诊断, 利用小波包分解提取故障特征, 基于SVM方法对气阀故障进行了识别, 利用网格搜索进行参数寻优, 搭建了小波包分解与支持向量机SVM联合诊断压缩机气阀故障的模型, 验证了支持向量机SVM诊断压缩机气阀故障的有效性。简化了传统由经验人员判断气阀故障类型的过程, 为压缩机气阀故障分析、气阀维修与更换等实际问题提供了理论依据。

The reciprocating compressor is an important equipment for petrochemical production, and its safe and stable operation is closely related to the operation states of the gas valve. For realizing the rapid diagnosis of the gas valve fault of the reciprocating compressor, the wavelet packet decomposition is used to extract the fault characteristics. Based on support vector machine (SVM) method, the gas valve fault is identified. The grid search method is used to optimize the parameters. The model for fault diagnosis of compressor gas valve is built, which is combined with wavelet packet decomposition and SVM. And the effectiveness of SVM in fault diagnosis of the compressor gas valve is verified, which simplifies the traditional process of judging the type of gas valve fault by experienced personnel, and provides a theoretical basis for practical problems such as compressor gas valve fault analysis, maintenance and replacement.