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.
Highlights
Condition monitoring on diesel engines is essential for their safety and reliability
A feature subset score based t-distributed stochastic neighbor embedding (t-SNE) data visualization method is proposed in this paper for the malfunction classification of diesel engines
The FSS-t-SNE method is compared with other methods, including linear methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), and non-linear methods such as isometric mapping (ISOMAP)
Summary
Condition monitoring on diesel engines is essential for their safety and reliability. An efficient approach for classifying the malfunctions of diesel engines from multi-sensor signals is the main task of a condition monitoring system. Different measurement methods were used for condition monitoring and diagnosis of diesel engines These measurement methods, including vibrations [4–7], instantaneous speed [8–10], oil analysis [11], acoustic emission [12,13], cylinder pressure [14–16], etc., have shown good performance in condition monitoring of the diesel engine. Of these signals, cylinder vibration signals are easy to obtain, and cylinder pressure signals can reflect the real combustion conditions of diesel engines. These two measurement methods have been widely used in diesel engine condition monitoring systems
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