An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.
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.
State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China. vikinimo@gmail.com.
Yiqing
Y
Li
State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China. ywang95@mail.xjtu.edu.cn.
Yu
Y
Wang
State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China. ziyy@mail.xjtu.edu.cn.
Yanyang
Y
Zi
State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China. zmqf117@stu.xjtu.edu.cn.
Mingquan
M
Zhang
data visualization; diesel engine; feature subset score; malfunction classification; multi-sensor signals