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Sensors 2015, 15(10), 26675-26693; doi:10.3390/s151026675

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 5 September 2015 / Revised: 11 October 2015 / Accepted: 12 October 2015 / Published: 21 October 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [950 KB, uploaded 21 October 2015]   |  

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. View Full-Text
Keywords: multi-sensor signals; data visualization; feature subset score; diesel engine; malfunction classification multi-sensor signals; data visualization; feature subset score; diesel engine; malfunction classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, Y.; Wang, Y.; Zi, Y.; Zhang, M. An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals. Sensors 2015, 15, 26675-26693.

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