【作者】 王宁；

【导师】 段礼祥；

【作者基本信息】 中国石油大学(北京) ， 机械工程， 2019， 硕士

【摘要】 往复压缩机是石油化工行业的重要设备,曲轴连杆机构是其最关键的部件,连杆大头瓦与曲轴轴颈因长期磨损容易导致两部件间的间隙过大,造成曲轴间隙故障,严重影响设备安全运行。本文针对曲轴间隙故障标签数据匮乏、故障响应特性研究不足及数据间概率分布存在差异的问题,提出了基于虚拟样机的往复压缩机曲轴间隙故障迁移诊断法,从运动副动力学建模、故障响应特性分析和故障的迁移诊断三个方面进行了研究,主要内容如下:(1)针对故障标签数据匮乏,提出了通过虚拟样机动力学仿真获取故障标签数据的方法。以往复压缩机试验台为物理样机,对运动副结构进行简化,然后应用Solid Works 2012软件建立一系列含有不同曲轴间隙值的运动副三维实体模型。通过分析确定了运动副动力学模型的约束条件、活塞负载、求解器、积分器以及接触参数,并利用ADAMS 2013仿真软件对不同工况下曲轴间隙故障进行动力学仿真,获得了故障的仿真数据。(2)针对故障响应特性研究不足,提出了仿真与实验相结合的曲轴间隙故障响应特性分析方法。首先在往复压缩机试验台上进行了曲轴间隙故障实验,然后将仿真结果与实验结果进行对比,验证了仿真结果的正确性。同时,对仿真信号的时域和频域分析表明,间隙的增大会加剧大头瓦与曲轴销之间的碰磨,且信号的时域峰值和频域内1k Hz-2k Hz、3k Hz-5k Hz频带内的能量均会随间隙值的增加而增大,表明了时域、频域响应特性能够反映曲轴间隙故障的产生与发展。(3)针对数据间存在差异的问题,提出了迁移成分分析(Transfer Component Analysis,TCA)与支持向量机(Support Vector Machine,SVM)相结合的故障诊断模型。用仿真数据训练TCA-SVM诊断模型,用实验数据来测试训练好的模型。结果表明,所提方法能更好地消除数据间的分布差异性,对不同数据来源、不同工况下的曲轴间隙故障进行诊断,正确率达到了87.19%,明显优于SVM直接分类等四种对比方法,实现了从仿真数据到实验数据及不同工况下曲轴间隙故障的迁移诊断。更多还原

【Abstract】 Reciprocating compressor is an important equipment in petrochemical industry,and crankshaft connecting rod mechanism is the most critical component.The connecting rod big end bearing shell and the crankshaft journal will lead to excessive clearance due to long-term wear and tear,which will cause crankshaft clearance fault and seriously affect the safe operation of equipment.Aiming at the problems of crankshaft clearance fault label data shortage,insufficient study on fault response characteristics and probability distribution vary between data,a transfer learning method for crankshaft clearance diagnosis of reciprocating compressor based on virtual prototype is proposed,which is studied from three aspects: motion pair dynamics modeling,fault response characteristics analysis and fault transfer diagnosis.The main contents are as follows:(1)Aiming at the lack of fault label data,the method of obtaining fault label data by dynamic simulation of virtual prototyping machine is proposed.The reciprocating compressor test-bed is used as a physical prototype to simplify the structure of the motion pair.Then a series of three-dimensional solid models of the motion pair with different crankshaft clearance values are established by using Solid Works 2012 software.The constraint conditions,piston load,solver,integrator and contact parameters of the motion pair dynamic model are determined by analysis.The dynamic simulation of crankshaft clearance fault under different working conditions is carried out by using ADAMS 2013 simulation software,and the simulation data of the fault are obtained.(2)Aiming at the insufficient study on fault response characteristics,the method of fault response characteristics analysis for crankshaft clearance based on simulation and experiment is proposed.Firstly,the crankshaft clearance fault experiment is carried outon the reciprocating compressor test-bed,and then the simulation results are compared with the experiment results to verify the correctness of the simulation results.At the same time,the time domain and the frequency domain analysis of the simulation signal show that the increase of the clearance will aggravate the contact friction between the big end bearing shell and crankshaft pin,and the peak value in time domain and the energy in frequency domain of 1k Hz-2k Hz and 3k Hz-5k Hz will raise with the increase of clearance value,which indicates that the response characteristics in time domain and frequency domain can reflect the generation and development of crankshaft clearance fault.(3)Aiming at the problem of differences between data,a fault diagnosis model based on the combination of transfer component analysis(TCA)and support vector machine(SVM)is proposed.TCA-SVM diagnosis model is trained with simulation data,and the trained model is tested with experimental data.The results show that the proposed method can better eliminate the differences of data distribution between different data sources and different working conditions,and the accuracy of crankshaft clearance fault diagnosis is improved.The accuracy reaches 87.19%,which is obviously superior to those of four contrast methods such as SVM direct classification.It realizes the transfer diagnosis of crankshaft clearance fault from simulation data to experimental data and under different working conditions.更多还原