Paper Info
Title
An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals.
Abstract
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.
Year
DOI
Venue
2015
10.3390/s151026675
SENSORS
Keywords
Field
DocType
multi-sensor signals,data visualization,feature subset score,diesel engine,malfunction classification
Data mining,Data visualization,Dimensionality reduction,Embedding,Vibration sensor,Visualization,A priori and a posteriori,Pressure sensor,Engineering,Diesel engine
Journal
Volume
Issue
ISSN
15
10.0
1424-8220
Citations 
PageRank 
References 
1
0.36
14
Authors
4
Name
Order
Citations
PageRank
Yiqing Li1504.50
Yu Wang2293.82
Yanyang Zi326825.13
Ming-quan Zhang454.29