An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.
An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.
复制标题
使用多传感器信号进行柴油机故障分类的增强数据可视化方法
DOI：
10.3390/s151026675
复制
发表时间：
2015-10-21
期刊：
影响因子：
--
通讯作者：
Zhang M
中科院分区：
文献类型：
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作者：
Li Y;Wang Y;Zi Y;Zhang M
柴油机的各种多传感器信号特征构成了一个复杂的高维数据集。T分布随机邻域嵌入(t-SNE)这一非线性降维方法为复杂高维数据的可视化提供了一种有效的方法。然而，不相关的特征会降低数据可视化的性能，因此应该先验地消除这些特征。针对从多传感器信号中采集到的高维数据，提出了一种基于特征子集得分的t-SNE(FSS-t-SNE)数据可视化方法。在该方法中，通过特征子集评分准则构造最优特征子集。然后在2维空间中对高维数据进行可视化。UCI数据集测试表明，FSS-t-SNE能够有效提高分类精度。以一台大功率船用柴油机为例，验证了所提出的柴油机故障分类方法的有效性。采用气缸振动传感器和气缸压力传感器采集多传感器信号。与传统的数据可视化方法相比，该方法在柴油机多故障分类中具有良好的可视化性能和较高的分类精度。
The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.