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Acoustic emission signal classification for gearbox failure detection.

机译：用于变速箱故障检测的声发射信号分类。

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摘要

The purpose of this research is to develop a methodology and technique to determine the optimal number of clusters in acoustic emission (AE) data obtained from a ground test stand of a rotating H-60 helicopter tail gearbox by using mathematical algorithms and visual inspection. Signs of fatigue crack growth were observed from the AE signals acquired from the result of the optimal number of clusters in a data set. Previous researches have determined the number of clusters by visually inspecting the AE plots from number of iterations. This research is focused on finding the optimal number of clusters in the data set by using mathematical algorithms then using visual verification to confirm it. The AE data were acquired from the ground test stand that simulates the tail end of an H-60 Seahawk at Naval Air Station in Patuxant River, Maryland. The data acquired were filtered to eliminate durations that were greater than 100,000 is and 0 energy hit data to investigate the failure mechanisms occurring on the output bevel gear. From the filtered data, different AE signal parameters were chosen to perform iterations to see which clustering algorithms and number of outputs is the best. The clustering algorithms utilized are the Kohonen Self-organizing Map (SOM), k-mean and Gaussian Mixture Model (GMM). From the clustering iterations, the three cluster criterion algorithms were performed to observe the suggested optimal number of cluster by the criterions. The three criterion algorithms utilized are the Davies-Bouldin, Silhouette and Tou Criterions. After the criterions had suggested the optimal number of cluster for each data set, visual verification by observing the AE plots and statistical analysis of each cluster were performed. By observing the AE plots and the statistical analysis, the optimal number of cluster in the data set and effective clustering algorithms were determined. Along with the optimal number of clusters and effective clustering algorithm, the mechanisms of each cluster can be determined from the statistical analysis as well. From the results, the 5 cluster output using the Kohonen SOM clustering algorithm showed the distinct separation of clusters. Using the determined number of clusters and the effective clustering algorithms, the AE data sets were analyzed for the fatigue crack growth. Recorded data from the mid test and end test of the data acquisition period were utilized. After each set of clusters were associated with different mechanisms dependent on their AE characteristics. It was possible to detect the increase in the activities of the fatigue crack data points. This indicates that the fatigue crack is growing as the acquisition continued on the H-60 Seahawk ground test stand and that AE has a good potential for early crack detection in gearbox components.
机译：这项研究的目的是开发一种方法和技术，以确定通过使用数学算法和视觉检查从旋转的H-60直升机尾部变速箱的地面测试台获得的声发射（AE）数据中的最佳簇数。从从数据集中最佳簇数的结果获取的AE信号中观察到疲劳裂纹扩展的迹象。以前的研究通过从迭代次数中目视检查AE图来确定簇的数量。这项研究的重点是通过使用数学算法，然后使用视觉验证对其进行确认，以找到数据集中的最佳簇数。 AE数据是从地面测试台获得的，该测试台模拟了马里兰州Patuxant River海军航空站的H-60海鹰的尾端。对获取的数据进行过滤以消除大于100,000 is的持续时间，并且对0个能量命中数据进行调查以研究输出锥齿轮上发生的故障机理。从滤波后的数据中，选择不同的AE信号参数来执行迭代，以查看哪种聚类算法和输出数量最佳。使用的聚类算法是Kohonen自组织图（SOM），k均值和高斯混合模型（GMM）。从聚类迭代中，执行了三种聚类准则算法，以根据准则观察建议的最佳聚类数。使用的三个标准算法是Davies-Bouldin，Silhouette和Tou Criterions。在标准提出了每个数据集的最佳聚类数之后，通过观察AE图进行视觉验证并对每个聚类进行统计分析。通过观察AE图和统计分析，确定了数据集中的最佳聚类数和有效的聚类算法。除了最佳聚类数和有效的聚类算法外，还可以从统计分析中确定每个聚类的机制。从结果来看，使用Kohonen SOM聚类算法的5个聚类输出显示了不同的聚类分离。使用确定的聚类数量和有效的聚类算法，分析了AE数据集的疲劳裂纹扩展。利用了数据采集期间的中级测试和最终测试的记录数据。之后，每组集群都根据其AE特性与不同的机制相关联。可以检测到疲劳裂纹数据点活动的增加。这表明，随着在H-60 Seahawk地面试验台上继续进行采购，疲劳裂纹正在增加，并且AE在变速箱部件的早期裂纹检测方面具有良好的潜力。

著录项

  • 作者

    Shishino, Jun.;

  • 作者单位

    Embry-Riddle Aeronautical University.;

  • 授予单位 Embry-Riddle Aeronautical University.;
  • 学科 Engineering Aerospace.
  • 学位 M.S.A.E.
  • 年度 2012
  • 页码 153 p.
  • 总页数 153
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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