Assessment of Diesel Engine Valve Performance Degradation Status Based on Synchroextracting Enhanced Generalized S-transform

doi:10.12382/bgxb.2023.0363

Abstract

Abstract:

The operating status of diesel engine changes with the performance degradation of valve as the valve clearance gradually increases during operation. It is difficult to accurately assessment the performance degradation status of valve by traditional status assessment methods. A diesel engine valve performance degradation status assessment method based on synchroextracting enhanced generalized S-transform (SEEGST) is proposed. The vibration signal reflecting the status of diesel engine is collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, a SEEGST time-frequency analysis method is proposed based on the synchroextracting algorithm and generalized S-transform to convert the vibration signal into a two-dimensional time-frequency map. MLP-Mixer model is used to extract the time-frequency image features for training,thus realizing the assessment of diesel engine status. The proposed method is compared with five traditional methods, namely SSGST-MLPMixer, GST-MLPMixer, SEEGST-ViT, SEEGST-2DCNN and FFT spectrum-1DCNN, by conducting the valve performance degradation experiment on a diesel engine status monitoring test bench. The experimental results show that the overall assessment accuracy of the proposed method reaches 98.96%, which can be effectively applied to the diesel engine valve performance degradation status assessment and provides a new idea for conducting the diesel engine valve performance degradation status assessment.

Key words: diesel engine, status assessment, synchroextracting enhanced generalized S-transform, MLP-Mixer

CLC Number:

TK428

LIU Zichang, BAI Yongsheng, LI Siyu, ZHANG Kun, LIU Min, JIA Xisheng. Assessment of Diesel Engine Valve Performance Degradation Status Based on Synchroextracting Enhanced Generalized S-transform[J]. Acta Armamentarii, 2024, 45(6): 2003-2016.

Figures/Tables 20

References 19

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状态序号	退化过程	气门间隙/mm	健康状态
1	正常	0.3	健康
2	气门间隙略微增大	0.4	注意
3	气门间隙增大	0.5	异常
4	气门间隙严重增大	0.7	恶化

状态序号	退化过程	气门间隙/mm	健康状态
1	正常	0.3	健康
2	气门间隙略微增大	0.4	注意
3	气门间隙增大	0.5	异常
4	气门间隙严重增大	0.7	恶化

层	输出
Rearrange-1	[-1, 196, 768]
Linear-2	[-1, 196, 512]
LayerNorm-3	[-1, 196, 512]
Conv1d-4	[-1, 784, 512]
GELU-5	[-1, 784, 512]
Dropout-6	[-1, 784, 512]
Conv1d-7	[-1, 196, 512]
Dropout-8	[-1, 196, 512]
PreNormResidual-9	[-1, 196, 512]
LayerNorm-10	[-1, 196, 512]
Linear-11	[-1, 196, 2048]
GELU-12	[-1, 196, 2048]
Dropout-13	[-1, 196, 2048]
Linear-14	[-1, 196, 512]
Dropout-15	[-1, 196, 512]
PreNormResidual-16	[-1, 196, 512]
︙	︙
LayerNorm-31	[-1, 196, 512]
Reduce-32	[-1, 512]
Linear-33	[-1, 4]

层	输出
Rearrange-1	[-1, 196, 768]
Linear-2	[-1, 196, 512]
LayerNorm-3	[-1, 196, 512]
Conv1d-4	[-1, 784, 512]
GELU-5	[-1, 784, 512]
Dropout-6	[-1, 784, 512]
Conv1d-7	[-1, 196, 512]
Dropout-8	[-1, 196, 512]
PreNormResidual-9	[-1, 196, 512]
LayerNorm-10	[-1, 196, 512]
Linear-11	[-1, 196, 2048]
GELU-12	[-1, 196, 2048]
Dropout-13	[-1, 196, 2048]
Linear-14	[-1, 196, 512]
Dropout-15	[-1, 196, 512]
PreNormResidual-16	[-1, 196, 512]
︙	︙
LayerNorm-31	[-1, 196, 512]
Reduce-32	[-1, 512]
Linear-33	[-1, 4]

模型	准确率/%		损失值
模型	训练集	验证集	训练集	验证集
SEEGST-MLPMixer	100.00	99.75	1.08×10^-4	1.43×10^-2
SSGST-MLPMixer	100.00	97.50	1.49×10^-4	1.00×10^-1
GST-MLPMixer	100.00	97.25	3.60×10^-4	9.27×10^-2
SEEGST-ViT	100.00	96.50	3.24×10^-3	1.32×10^-1
SEEGST-2DCNN	94.73	94.75	2.44×10^-1	2.37×10^-1
FFT spectrum-1DCNN	91.86	92.50	5.37×10^-1	6.33×10^-1