摘要
悬架系统直接关系到车辆的安全性、平顺性和操稳性,由于路面激励是随机激励,对悬架系统的状态监测一直是研究难点。该文提出一种新的悬架状态监测方法,利用仅需输出的平均相关随机子空间法识别模态参数,再通过模态参数变化对故障造成的悬架刚度变化进行监测。首先对平均相关随机子空间法在较高阻尼比下的识别效果进行分析,验证算法在悬架监测中的可行性;然后基于车辆七自由度振动模型对模态参数进行仿真识别,分析路面激励及噪声对识别结果的影响,并提出基于振型和模态能量的监测方法;最后设计利用9轴MEMS惯性传感器的试验方案对正常及故障状态进行监测,验证方法的可信度。
The performance of suspension system is directly related to the vehicle safety, riding comfort and handling stability. However, the road surface is a kind of random excitation, which places many difficulties in research on the condition monitoring of suspension system. Based on the average correlation signal based stochastic subspace identification(ASC-SSI), a novel method was presented to identify the modal parameters of suspension system in this article. The average correlation signal based stochastic subspace identification method was used to identify model parameters and the changes in suspension stiffness caused by changes of model parameters are monitored. Firstly, the validation of this algorithm was confirmed in a high damping ratio situation.Then, based on an established seven degree of freedom dynamic model, the modal parameters of suspension system were identified to analyze the influences of excitation from road roughness and strong noise to identification results, and then a monitoring method based on mode shape and modal energy was proposed.Finally, a test scheme using 9-axis MEMS inertial sensor was designed to monitor the normal and faulty condition and verify the validity and feasibility of the proposed method.
出处
《中国测试》
北大核心
2017年第5期138-144,共7页
China Measurement & Test
关键词
模态参数识别
悬架系统
平均相关随机子空间法
状态监测
MEMS
modal parameter identification
suspension system
average correlation signal based stochastic subspace identification
condition monitoring
MEMS
