Abstract
The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.
Highlights
The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets
The results showed that the convolutional neural network-based (CNN) method can accurately detect a complete misfire in one or two cylinders when the diesel engine is operating under steady-state conditions
A novel method is proposed to establish the performance models of marine diesel engines based on the Deep Neural Network (DNN) method coupled with Virtual Sample Generation (VSG)
Summary
In order to reducing the cost of experimental modeling, the DNN coupled with VSG technology is proposed to establish the accurate performance models of the diesel. The influence law of diesel engine parameters on the performance is obtained based on the proposed models. The effective prediction of C O2, CO, and NOx emissions was achieved after the continuation of the modeling system based on experimental data, which provides a reference for the optimization of diesel engines. Shin et al.[16] combined the deep neural network and Bayesian method to optimizing the diesel engine parameters and predicting the NOx transient emission, which enhanced the model stability and accuracy significantly. A novel method is proposed to establish the performance models of marine diesel engines based on the Deep Neural Network (DNN) method coupled with Virtual Sample Generation (VSG). The uncertainty of a dependent variable is calculated using errors involved in measuring independent parameters such as power, speed, and lubricating oil temperature. The uncertainty value can be derived by Eq (3)[17,22]
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