An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals
AbstractThe 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
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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.
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(10):26675-26693.Chicago/Turabian Style
Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan. 2015. "An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals." Sensors 15, no. 10: 26675-26693.