Fault Diagnosis Method for Diesel Engine Based on Texture Analysis

被引：0

作者：

Liu Z. ^{[1
]}

Li S. ^{[1
]}

Pei M. ^{[1
]}

Liu J. ^{[1
]}

Meng S. ^{[1
]}

Wu W. ^{[1
]}

机构：

[1] Shijiazhuang Campus of Army Engineering University, Hebei, Shijiazhuang

来源：

Binggong Xuebao/Acta Armamentarii | 2024年 / 45卷 / 02期

关键词：

diesel engine; modified hierarchical decomposition; non-dominated sorting genetic algorithm-Π; support vector machine; texture analysis;

D O I：

10.12382/bgxb.2022.1021

中图分类号：

学科分类号：

摘要：

Diesel engine fault feature extraction is a key step in the process of fault identification, which is directly related to the accuracy and timeliness of identification. The texture analysis theory is applied to diesel engine fault feature extraction, and a fault feature extraction method based on modified hierarchical decomposition (MHD) and grayscale image processing is proposed. A single one-dimensional vibration signal sample is decomposed into multiple one-dimensional sub-signals and converted into a grayscale image separately using MHD. The features from accelerated segment test (FAST) algorithm is used to detect the feature points of grayscale image; the image is convolved by the real part of Gabor filter bank, and the histograms are computed as feature vectors using the responses of the feature points. In order to test the ability of the fault features extracted by the proposed method to recognize the different fault types of diesel engine, non-dominated sorting genetic algorithm-Π (NSGA-Π) and support vector machine (SVM) are introduced for fault status recognition. Preset fault experiments are carried out through a experimental bench to compare the proposed method with the traditional method. The experimental results show that the proposed method has the highest fault diagnosis accuracy and provides a new idea for diesel engine fault diagnosis. © 2024 China Ordnance Industry Corporation. All rights reserved.

引用

页码：684 / 694

页数：10

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