基于传递路径分析的车身阻尼材料拓扑优化 |
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引用本文: | 王振亚,戚晓利,吴保林. 基于传递路径分析的车身阻尼材料拓扑优化[J]. 噪声与振动控制, 2009, 39(2): 167-172 |
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作者姓名: | 王振亚 戚晓利 吴保林 |
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作者单位: | ( 1. 重庆大学机械与运载工程学院,重庆400030;2. 东风柳州汽车有限公司,广西柳州545005 )
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摘 要: | 以某乘用车怠速工况下的车内噪声为研究对象,建立内饰车身的声-固耦合有限元模型,施加实测的加速度激励预测车内噪声响应。通过有限元模型获取系统传递函数,结合实测加速度激励建立传递路径分析模型,分析怠速工况下驾驶员右耳位置121 Hz频率处各路径的声学噪声贡献情况,以贡献量较大的路径为板件贡献量分析的激励输入位置,确定后地板为铺设阻尼的目标板件。以121 Hz处驾驶员右耳声压最小为目标,建立拓扑优化模型,对后地板阻尼进行布局优化。结果表明,怠速工况下121 Hz峰值频率处驾驶员右耳声压级下降5.59 dB(A),传递路径分析对阻尼结构优化设计具有一定指导作用。
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关 键 词: | 振动与波 传递路径分析 板件贡献量分析 阻尼材料 拓扑优化 |
收稿时间: | 2018-05-29 |
修稿时间: | 2021-06-24 |
A Rolling Bearing Fault Diagnosis Method based on ASL-Isomap Manifold Learning |
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Abstract: | Aiming at solving the problem of over-high dimensions and redundancy in the mixed fault feature set of rolling bearings, a fault diagnosis method of rolling bearing based on adaptive self-organizing incremental neural network landmark Isomap (ASL-Isomap) was proposed. Firstly, the fault features of vibration signals were extracted from time domain, frequency domain, time-frequency domain and complex domain, and the high-dimensional hybrid domain fault feature set was constructed. Secondly, the ASL-Isomap method was used to reduce the dimension of the high-dimensional hybrid domain fault feature set, and the low-dimensional and sensitive feature subset was extracted. Finally, low-dimensional features were input into a kernel extreme learning machine (KELM) classifier to recognize fault types. ASL-Isomap method integrated the advantages of adaptive neighborhood construction and SOINN landmarks selection, and could more effectively explore the low-dimensional essential manifold of data set. Experimental results of fault diagnosis on cylindrical roller bearings show the effectiveness and advantage of the proposed method. |
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