基于表面辐射声信号的柴油机进气及齿轮故障诊断  

Radiated noise-based diagnosis for diesel engine intake and gear faults

作　　者：李斌[1,2] 林杰威 朱小龙[1] 林耕毅 张益铭 张俊红 LI Bin;LIN Jiewei;ZHU Xiaolong;LIN Gengyi;ZHANG Yiming;ZHANG Junhong(State Key Laboratory of Engines at Tianjin University,Tianjin 300350,China;Weichai Lovol Intelligent Agricultural Technology Co.,Ltd.,Weifang,Shandong 261220,China;School of Mechanical Engineering,Tianjin Renai College,Tianjin 301636,China)

机构地区：[1]天津大学先进内燃动力全国重点实验室,天津300350 [2]潍柴雷沃智慧农业科技股份有限公司,山东潍坊261220 [3]天津仁爱学院机械工程学院,天津301636

出　　处：《排灌机械工程学报》2024年第8期843-850,共8页Journal of Drainage and Irrigation Machinery Engineering

基　　金：国家重点研发计划项目(2021YFD2000303)。

摘　　要：利用声振信号进行发动机故障诊断过程中,部分故障激励仅在发动机表面特定位置的振动中有较强响应,振动测点要求高,需要接触测量,部分场景难以实现.为此,提出了一种以表面辐射声为媒介、以自适应变分模态提取(adaptive variational mode extraction,AVME)进行预处理的柴油机进气故障和齿轮故障诊断方法.开展了某直列六缸重型柴油机的进气滤清器堵塞、气门间隙异常和正时齿轮损伤3类故障状态的台架试验,获取了不同故障程度下发动机表面辐射噪声.基于改进的AVME方法,实现噪声信号本征模函数(intrinsic mode function,IMF)的最优分解,通过计算IMF与原信号间的互相关系数,提取高相关IMF构成故障诊断输入.经预处理后,声信号故障特征得到有效增强,再输入到麻雀搜索算法优化支持向量机模型(support vector machine model optimized by sparrow search algorithm,SSA-SVM),进行特征参量和模型参数协同优化可以获得更好的诊断精度.试验验证表明,无需在半消声室测试,仅使用单通道声信号对3类11种程度的进气系统和齿轮故障进行诊断,前端噪声准确率最高(98.89%),顶部噪声准确率最低(88.78%);使用前、顶、后三通道噪声数据后,诊断精度可提升至99.57%.研究结论为基于声信号等非接触测量的发动机故障诊断提供了参考.Use the acoustic vibration signal for the engine fault diagnosis process,some fault excitations only express a strong response in the specific vibration on the engine surface,and the vibration measurement points require high requirements with contact measurement,which is difficult to achieve under some scenarios.Therefore,a diesel engine intake fault and gear fault diagnosis method were proposed using surface radiated sound as the medium and adaptive variational mode extraction(AVME)as the preprocessing method.Bench experiments were carried out under three fault conditions of a 6-cylinder in-line heavy-duty diesel engine,namely:air filter blockage,abnormal valve clearance and timing gear damage,and the engine surface acoustic signal under different fault degrees was obtained.Based on the improved AVME method,the optimal decomposition of the intrinsic mode function(IMF)of the acoustic signal was achieved.By calculating the mutual relationship between IMF and the original signal,the highly correlated IMF was extracted to constitute the classifier input.By AVME,the fault acoustic features were effectively enhanced,and input into the support vector machine model optimized by sparrow search algorithm(SSA-SVM),and the collaborative optimization of feature parameters and model parameters can achieve better diagnosis accuracy.The experimental verification results show that without the need for testing in a semi-anechoic chamber,only a single-channel acoustic signal is used to diagnose three types of 11 degrees of the intake system and gear faults,the accuracy rate of the front-end acoustic and the top-side acoustic signals are the highest(98.89%)and the lowest(88.78%),respectively.After using the front,top,and rear acoustic data,the diagnostic accuracy rate can reach 99.57%.The research results provide a reference for engine fault diagnosis based on non-contact measurement methods such as acoustic signals.

关 键 词：柴油机 声信号 故障诊断 自适应变分模态提取 支持向量机 

分 类 号：TK428[动力工程及工程热物理—动力机械及工程]