基于故障案例学习的设备健康评价方法研究

从设备状态监测数据中分析、提取故障特征信息,准确、快速地识别设备健康状态,对于开展预测性维修,确保设备运行可靠性、安全性非常重要。以离心压缩机为研究对象,以正常状态的振动监测原始数据为参考数据,构建了正常状态原始信号和实时监测信号的相关性健康指数模型、相干性健康指数模型、谱距离健康指数模型,在针对健康指数模型开展大量故障案例学习、健康度分布统计分析基础上,制定了设备健康评级准则,形成数据驱动的机械设备健康评价方法,揭示了离心压缩机健康度表征与运行状态的映射关系。应用轴承试验数据和离心压缩机转子不平衡故障案例数据,分别验证构建的设备健康指数模型和健康评级准则的准确性、适用性。结果表明,构建的健康指数模型能较好地表征设备运行状态,与有效值和峰-峰值固定阈值报警方法相比,构建的机械设备健康评级准则对于指导预测性维修更有实践意义。

Research on Equipment Health Evaluation Method Based on Fault Case Learning

Analyzing and extracting fault feature information from equipment condition monitoring data to accurately and quickly identify equipment health status is very important for carrying out predictive maintenance and ensuring equipment operation reliability and safety. Taking the centrifugal compressor as the research object and taking the vibration monitoring data of the normal state of the equipment as the reference data,the correlation health index model,the coherence health index model,and the spectral distance health index model applying with the normal state original signal and the real-time monitoring signal are constructed. Based on a large number of failure case studies for the three health index models and statistical analysis of health degree distribution,equipment health rating criteria are developed,a data-driven mechanical equipment health evaluation method is formed,and the mapping relationship between centrifugal compressor health degree characterization and running status is revealed. The bearing experiment data and centrifugal compressor rotor imbalance failure data are used to verify the accuracy and applicability of the constructed equipment health index model and health rating criteria,respectively. The results show that the constructed health index models can better characterize the operating state of the equipment. Compared with the fixed threshold alarm methods of effective value and peak-peak,the constructed health evaluation criteria for mechanical equipment is more practical for guiding predictive maintenance.