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 dat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/10/26675 |
| _version_ | 1798004912148185088 |
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| author | Yiqing Li Yu Wang Yanyang Zi Mingquan Zhang |
| author_facet | Yiqing Li Yu Wang Yanyang Zi Mingquan Zhang |
| author_sort | Yiqing Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T12:30:56Z |
| format | Article |
| id | doaj.art-2b0266d10d58416eb7dba92630cb5683 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:30:56Z |
| publishDate | 2015-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2b0266d10d58416eb7dba92630cb56832022-12-22T04:23:45ZengMDPI AGSensors1424-82202015-10-011510266752669310.3390/s151026675s151026675An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor SignalsYiqing Li0Yu Wang1Yanyang Zi2Mingquan Zhang3State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, ChinaThe 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.http://www.mdpi.com/1424-8220/15/10/26675multi-sensor signalsdata visualizationfeature subset scorediesel enginemalfunction classification |
| spellingShingle | Yiqing Li Yu Wang Yanyang Zi Mingquan Zhang An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals Sensors multi-sensor signals data visualization feature subset score diesel engine malfunction classification |
| title | An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals |
| title_full | An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals |
| title_fullStr | An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals |
| title_full_unstemmed | An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals |
| title_short | An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals |
| title_sort | enhanced data visualization method for diesel engine malfunction classification using multi sensor signals |
| topic | multi-sensor signals data visualization feature subset score diesel engine malfunction classification |
| url | http://www.mdpi.com/1424-8220/15/10/26675 |
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