针对目前在优化传感器布置时对齿轮箱可能发生的故障信号是否能被识别与分离研究较少的情况，将故障可诊断性应用于齿轮箱传感器优化布置中，并运用奇异值比值、故障可诊断性、平均加速度幅值三个评价准则构成的综合指标评价不同布置方案，最终确定最优传感器布置方案。首先，对齿轮箱进行模态分析，提取模态振型；再运用K均值聚类算法根据自由度在重要模态中振型的动力相似性进行分类；其次利用有效独立平均加速度幅值法初选测点；然后，在这些节点位置处测得可能出现的故障信号，对其进行快速傅里叶变换，得到相应故障的频谱图，利用核密度估计求取对应故障的密度函数，再利用K-L散度判断各位置处的故障可诊断性；最后，运用综合指标评价，确定最优方案。通过ZDH10型齿轮箱故障诊断试验台验证了所提方法的可行性。
Abstract
At present, when optimizing the position of a sensor, there is little research on whether the possible fault signal of a gearbox can be identified and separated.In this work, the fault diagnosability was applied to the optimal sensors placement of a gear box, and the different placement schemes were evaluated by using the comprehensive index composed of three evaluation criteria: singular value ratio, fault diagnosability, and average acceleration amplitude, and the optimal sensor placement scheme was determined.Firstly, the modal analysis of the gearbox was carried out to extract the modal modes, and then the K-means clustering algorithm was used to classify the dynamic similarity of shape modes of degrees of freedom at important modes.Secondly, the effective independent average acceleration amplitude method was used to select the initial measuring points.Then, the possible fault signals were measured at the positions of these nodes, and the corresponding fault spectrum was obtained by the fast Fourier transform.The density function of the corresponding fault was obtained by using the Kernel density estimation, and then the fault diagnosability at each position was judged by Kullback-Leibler divergence.Finally, the comprehensive index was used to evaluate the optimal scheme.The feasibility of the proposed method was verified on the ZDH10 gearbox fault diagnosis setup.
关键词
齿轮箱 /
传感器优化布置 /
K均值聚类 /
核密度估计 /
K-L散度 /
故障可诊断性
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Key words
gear box /
optimal sensor placement /
K-means clustering /
kernel density estimation /
Kullback-Leibler divergence /
fault diagnosability
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