Abstract
Feature selection is an essential step for data classification used in fault detection and diagnosis processes. In this work, a new approach is proposed, which combines a feature selection algorithm and a neural network tool for leak detection and characterization tasks in diesel engine air paths. The Chi square classifier is used as the feature selection algorithm and the neural network based on Levenberg-Marquardt is used in system behavior modeling. The obtained neural network is used for leak detection and characterization. The model is learned and validated using data generated by xMOD. This tool is used again for testing. The effectiveness of the proposed approach is illustrated in simulation when the system operates on a low speed/load and the considered leak affecting the air path is very small.
Highlights
In order to reduce air pollution caused by automobile engines, several standards have been introduced.The first standard was proposed by the California Air Resources Board in 1970
A new method for feature selection based on mutual information for fault detection and identification is proposed in [17]
In order to illustrate the advantage of feature selection, the Mean Squared Error (MSE) values are jointly shown with their training run times
Summary
In order to reduce air pollution caused by automobile engines, several standards have been introduced. A new method for feature selection based on mutual information for fault detection and identification is proposed in [17] Their algorithm is based on two principle stages: the variables are sorted based on their shared mutual information with the class variable and secondly the more informative variables are chosen by taking into account the classification error rate. Wang [19] introduces a neural network approach to vibration feature selection in mechanical systems fault detection He proposes an artificial intelligence methodology for mechanical fault detection using vibration data, which includes intelligent feature optimization. A new methodology dealing with the problem of detecting and characterizing small leaks in diesel air paths is developed To achieve this goal, a new scheme based on a neural network technique is proposed. These results are discussed and commented in order to illustrate the effectiveness of leakage detection and characterization
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