Fault Diagnosis Method for Diesel Engine Based on Texture Analysis

doi:10.12382/bgxb.2022.1021

Abstract

Abstract:

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-Ⅱ (NSGA-Ⅱ) 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.

Key words: diesel engine, texture analysis, modified hierarchical decomposition, non-dominated sorting genetic algorithm-Ⅱ, support vector machine

CLC Number:

TH113.1

LIU Zichang, LI Siyu, PEI Mochao, LIU Jie, MENG Shuo, WU Weiyi. Fault Diagnosis Method for Diesel Engine Based on Texture Analysis[J]. Acta Armamentarii, 2024, 45(2): 684-694.

Figures/Tables 16

Fig.1 An illustration for decomposing signal of MHD

Fig.2 Feature point detection using FAST algorithm

Fig.3 Conversion of one-dimensional signal into two-dimensional matrix

Fig.4 Example of proposed feature extraction method

Fig.5 Flowchart of joint optimization for feature selection and state identification using NSGA-Ⅱ-SVM

Fig.6 Diesel engine condition monitoring test bench

Table 1 Diesel engine preset fault mode

序号	故障模式	序号	故障模式
1	正常状态	4	空气滤清器堵塞
2	一缸失火	5	喷油泵供油不足
3	二缸失火	6	一、二缸失火

Fig.7 Time-domain waveforms for 6 fault statuses

Fig.8 Time-domain waveform(above) and image representation(below) of each sub-signal

Fig.9 The number of feature points of the type of failure at different t

Fig.10 Visualizable relationships among classification accuracy and the parameters ksize, σ and t shown from two angles

Fig.11 Classification accuracy with different values of ksize, σ and t

Fig.12 Identification accuracies of 4 methods in Instance 2

Table 2 Identification accuracy statistics of 4 methods in Instance 2 %

方法	最大值	最小值	平均值
1	85.59	84.26	85.06
2	88.07	86.03	86.79
3	97.34	96.07	97.05
4	95.28	93.71	94.68

Fig.13 Comparison results of 4 methods in Instance 3

Table 3 Fault status identification results of 4 methods in Instance 3 %

方法	最大值	最小值	平均值
本文方法	97.76(11)	96.17(8)	97.18
LBP-NSGA-Ⅱ-SVM	95.61(19)	93.22(15)	94.27
1D-LTP-NSGA-Ⅱ-SVM(上模式)	97.08(16)	95.16(12)	96.34
1D-LTP-NSGA-Ⅱ-SVM(下模式)	96.63(14)	94.73(12)	95.51

References 23

[1]	杨攀涛, 崔涛, 赵彦凯. 电动增压器对柴油发动机低速稳态性能的影响[J]. 兵工学报, 2021, 42(9): 1829-1837. doi: 10.3969/j.issn.1000-1093.2021.09.003
	YANG P T, CUI T, ZHAO Y K. Effect of electric supercharger on low-speed steady-state performance of diesel engine[J]. Acta Armamentarii, 2021, 42(9): 1829-1837. (in Chinese)
[2]	仝蕊, 康建设, 孙健, 等. 基于局部特征尺度分解与复合谱分析的齿轮性能退化特征提取[J]. 兵工学报, 2019, 40(5): 1093-1102. doi: 10.3969/j.issn.1000-1093.2019.05.023
	TONG R, KANG J S, SUN J, et al. Gear performance degrada-tion feature extraction based on local characteristic-scale de-composition and modified composite spectrum analysis[J]. Acta Armamentarii, 2019, 40(5): 1093-1102. (in Chinese) doi: 10.3969/j.issn.1000-1093.2019.05.023
[3]	杨大为, 赵永东, 冯辅周, 等. 基于参数优化变分模态分解和多尺度熵偏均值的行星变速箱故障特征提取[J]. 兵工学报, 2018, 39(9): 1683-1691. doi: 10.3969/j.issn.1000-1093.2018.09.003
	YANG D W, ZHAO Y D, FENG F Z, et al. Planetary gearbox fault feature extraction based on parameter optimized varia-tional mode decomposition and partial mean of multi-scale en-tropy[J]. Acta Armamentarii, 2018, 39(9): 1683-1691. (in Chinese)
[4]	李良钰, 苏铁熊, 马富康, 等. 基于集合经验模态分解-支持向量机的高压共轨系统故障诊断方法[J]. 兵工学报, 2022, 43(5): 992-1001. doi: 10.12382/bgxb.2021.0155
	LI L Y, SU T X, MA F K, et al. Fault diagnosis method of high-pressure common rail system based on EEMD-SVM[J]. Acta Armamentarii, 2022, 43(5): 992-1001. (in Chinese) doi: 10.12382/bgxb.2021.0155
[5]	饶响, 盛晨兴, 郭智威. 不同表面纹理结构对柴油机缸套-活塞环摩擦磨损性能的影响研究[J]. 兵工学报, 2018, 39(2): 356-363. doi: 10.3969/j.issn.1000-1093.2018.02.019
	RAO X, SHENG C X, GUO Z W. Study on the influence of different surface texture structure on friction and wear per-formance of cylinder liner-piston ring of diesel engine[J]. Acta Armamentarii, 2018, 39(2): 356-363. (in Chinese)
[6]	KAPLAN K, KAYA Y, KUNCAN M, et al. An improved feature ex-traction method using texture analysis with LBP for bearing fault diagnosis[J]. Applied Soft Computing, 2020, 87: 106019. doi: 10.1016/j.asoc.2019.106019 URL
[7]	ZHENG H, CHENG G, LI Y, et al. A new fault diagnosis method for planetary gear based on image feature extraction and bag-of-words model[J]. Measurement, 2019, 145: 1-13. doi: 10.1016/j.measurement.2019.05.046 URL
[8]	KUNCAN M, KAPLAN K, MⅰNAZ M R, et al. A novel feature ex-traction method for bearing fault classification with one dimen-sional ternary patterns[J]. ISA Transactions, 2020, 100: 346-357. doi: 10.1016/j.isatra.2019.11.006 URL
[9]	RUIZ M, MUJICA L E, ALFEREZ S, et al. Wind turbine fault de-tection and classification by means of image texture analysis[J]. Mechanical Systems and Signal Processing, 2018, 107: 149-167. doi: 10.1016/j.ymssp.2017.12.035 URL
[10]	彭泊涵, 欧阳杨, 张良. 几何与Gabor纹理特征的机载LiDAR点云建筑物提取[J]. 测绘科学, 2022, 47(6):119-126.
	PENG B H, OUYANG Y, ZHANG L. Airborne LiDAR point cloud building extraction with geometric and Gabor texture features[J]. Science of Surveying and Mapping, 2022, 47(6):119-126. (in Chinese)
[11]	LI Y B, LI G Y, YANG Y T, et al. A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy[J]. Me-chanical Systems and Signal Processing, 2018, 105: 319-337.
[12]	YANG C, JIA M P. Health condition identification for rolling bear-ing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine-based binary tree[J]. Structural Health Monitoring, 2021, 20(1): 151-172. doi: 10.1177/1475921720923973 URL
[13]	卢胜男, 李小和. 基于对称FAST特征的车辆目标检测方法[J]. 微电子学与计算机, 2020, 37(2):37-42.
	LU S N, LI X H. Vehicle target detection method based on symmetric FAST feature[J]. Microelectronics and Com-puter, 2020, 37(2):37-42. (in Chinese)
[14]	VIJAYAN T, SANGEETHA M, KUMARAVEL A, et al. Gabor filter and machine learning based diabetic retinopathy analysis and detection[J]. Microprocessors and Microsystems, 2020: 103353.
[15]	WU J H, WEI P J, YUAN X F, et al. A new Gabor filter-based method for automatic recognition of hatched residential ar-eas[J]. IEEE Access, 2019: 40649-40662.
[16]	ROSTEN E, PORTER R, DRUMMOND T. Faster and better: a machine learning approach to corner detection[J]. IEEE Trans-actions on Software Engineering, 2010, 32(1): 105-119.
[17]	BIANCONI F, FERNÁNDEZ A. Evaluation of the effects of Gabor filter parameters on texture classification[J]. Pattern recogni-tion, 2007, 40(12): 3325-3335.
[18]	ZHANG Y K, LI W J, ZHANG L P, et al. Adaptive learning Gabor filter for finger-vein recognition[J]. IEEE Access, 2019, 7: 159821-159830. doi: 10.1109/ACCESS.2019.2950698
[19]	BAI Y L, YANG J W, WANG J H, et al. Image representation of vi-bration signals and its application in intelligent compound fault diagnosis in railway vehicle wheelset-axlebox assemblies[J]. Mechanical Systems and Signal Processing, 2021, 152: 107421. doi: 10.1016/j.ymssp.2020.107421 URL
[20]	ZHANG W, PENG G L, LI C H. Bearings fault diagnosis based on convolutional neural networks with 2-D representation of vi-bration signals as input[C]//Proceedings of the 2016 the 3rd In-ternational Conference on Mechatronics and Mechanical Engi-neering. Shanghai, China: EDP Sciences, 2017, 95: 13001.
[21]	WANG S P, ZHAO D M, YUAN J Z, et al. Application of NSGA-Ⅱ algorithm for fault diagnosis in power system[J]. Electric Power Systems Research, 2019, 175:105893. doi: 10.1016/j.epsr.2019.105893 URL
[22]	JIA W, HU R X, LEI Y K, et al. Histogram of oriented lines for palmprint recognition[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014, 44(3): 385-395. doi: 10.1109/TSMC.6221021 URL
[23]	KAPLAN K, KAYA Y, KUNCAN M, et al. An improved feature ex-traction method using texture analysis with LBP for bearing fault diagnosis[J]. Applied Soft Computing, 2020, 87:106019. doi: 10.1016/j.asoc.2019.106019 URL