Blockage Location Algorithm of Multi-cylinder Fuel Injectors Based on Stacked Sparse Autoencoder

doi:10.12382/bgxb.2023.0764

Abstract

Abstract:

The quality of the fuel injection system directly affects the working process and performance of diesel engines. The traditional fault diagnosis methods are difficult to accurately locate the faulty injectors when the blocking faults occur in different injectors of multi-cylinder engines with varying degrees of fault. A fault location algorithm based on stacked sparse autoencoder (SSAE) is proposed. The deep features of rail pressure signals are extracted by SSAE when the fuel injectors at different positions experience the blocking faults, and softmax network is used to locate the faulty injectors. The influence of algorithm hyperparameters on algorithm accuracy is studied by takin one-dimensional rail pressure signal as input and faulty injector position as output. The final results indicate that this algorithm can accurately locate the fuel injector with blocking faults, and the locating accuracy is not affected by the degree of blockage. The fault diagnosis accuracy can reach 96.7%.

Key words: high-pressure common rail, blockage of different fuel injectors, stacked sparse autoencoder, fault location

CLC Number:

TK428

WANG Jian, HUANG Ying, GAO Xiaoyu, WANG Tuo, WANG Xu, HUI Jiahe. Blockage Location Algorithm of Multi-cylinder Fuel Injectors Based on Stacked Sparse Autoencoder[J]. Acta Armamentarii, 2024, 45(10): 3706-3717.

Figures/Tables 23

Fig.1 Simulation model of high pressure common rail system

Table 1 High pressure common rail system parameters

部件	参数	数值
	喷孔直径/mm	0.12
	喷孔个数	6
	针阀升程/mm	0.25
喷油器	控制阀升程/mm	0.05
	喷嘴压力室容积/mm³	0.2
	进油孔直径/mm	0.28
	出油孔直径/mm	0.31
	共轨管直径/mm	9.5
共轨管	共轨管长度/mm	250
	高压油管内径/mm	2
燃油泵	柱塞直径/mm	7
	柱塞行程/mm	12

Fig.2 Verificated results of fuel injector inlet pressure under different rail pressures

Fig.3 Structure diagram of SSAE

Fig.4 Flowchart of locating the injector blocking faults based on SSAE

Table 2 Simulation parameters for blocking faults of fuel injectors in each cylinder

故障类别	堵塞程度
正常	正常
1缸喷油器堵塞	堵塞25%,堵塞 50%,堵塞75%
2缸喷油器堵塞
3缸喷油器堵塞
4缸喷油器堵塞

Fig.5 The impact of blocked fuel injectors on rail pressure in each cylinder at 150MPa

Fig.6 Change of SSAE training error with iteration times

Fig.7 Visualization of fault characteristics of fuel injectors blocking in each cylinder

Table 3 Hyperparameter settings for studying the influence of network layers

试验组	n	ρ	epoch
1	2	0.1	400
2	3	0.1	400
3	4	0.1	400
4	5	0.1	400

Fig.8 Comparison of feature extraction results for different network layers

Table 4 The influence of network layers

试验组	n	轮廓系数	训练时间/s
1	2	0.6695	24.53
2	3	0.7165	39.05
3	4	0.7218	50.19
4	5	0.7242	54.74

Table 5 Hyperparameter settings for studying the influence of sparse factors

试验组	n	ρ	epoch
1	3	0.1	400
2	3	0.2	400
3	3	0.3	400
4	3	0.4	400

Table 6 The influence of sparse factor

试验组	ρ	轮廓系数	训练时间/s
1	0.1	0.7165	39.05
2	0.2	0.9054	38.75
3	0.3	0.5433	39.24
4	0.4	0.4727	39.61

Fig.9 Comparison of feature extraction results for different sparse factors

Table 7 Hyperparameter settings for studying the influence ofiteration times

试验组	n	ρ	epoch
1	3	0.2	50
2	3	0.2	100
3	3	0.2	200
4	3	0.2	400

Table 8 The influence of iteration times

试验组	epoch	轮廓系数	训练时间/s
1	50	0.8089	5.42
2	100	0.8400	9.12
3	200	0.9031	12.51
4	400	0.9054	38.75

Fig.10 Comparison of feature extraction results for different iteration times

Table 9 Hyperparameter setting of SSAE network

N	ρ	epoch
3	0.2	200

Fig.11 Diagnosis results of SSAE model training and testing sets

Fig.12 Diagnosis results of SSAE model training and testing sets at 90MPa

Fig.13 Diagnosis results of SSAE model training and esting sets at 120MPa

Table 10 Comparison of diagnostic results of different algorithms

算法	训练精度/%	训练时间/s	诊断用时/s	测试精度/%
AE	76.4	13.04	0.73	80.8
SAE	93.1	4.94	0.7	86.7
SSAE	100	14.8	0.93	96.7

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