LAPSE:2020.0084
Published Article
LAPSE:2020.0084
Fault Diagnosis of the Blocking Diesel Particulate Filter Based on Spectral Analysis
Shuang-xi Liu, Ming Lü
January 19, 2020
Diesel particulate filter is one of the most effective after-treatment techniques to reduce Particulate Matters (PM) emissions from a diesel engine, but the blocking Diesel Particulate Filter (DPF) will seriously affect the engine performance, so it is necessary to study the fault diagnosis of blocking DPF. In this paper, a simulation model of an R425DOHC diesel engine with wall-flow ceramic DPF was established, and then the model was verified with experimental data. On this basis, the fault diagnosis of the blocking DPF was studied by using spectral analysis on instantaneous exhaust pressure. The results showed that both the pre-DPF mean exhaust pressure and the characteristic frequency amplitude of instantaneous exhaust pressure can be used as characteristic parameters of monitoring the blockage fault of DPF, but it is difficult to monitor DPF blockage directly by instantaneous exhaust pressure. In terms of sensitivity, the characteristic frequency amplitude of instantaneous exhaust pressure is more suitable as a characteristic parameter to monitor DPF blockage than mean exhaust pressure. This work can lay an important theoretical foundation for the on-board diagnosis of DPF.
Keywords
blockage, DPF, exhaust pressure, fault diagnosis, spectral analysis
Suggested Citation
Liu SX, Lü M. Fault Diagnosis of the Blocking Diesel Particulate Filter Based on Spectral Analysis. (2020). LAPSE:2020.0084
Author Affiliations
Liu SX: National Engineering Laboratory for Mobile Source Emission Control Technology, China Automotive Technology & Research Center, Tianjin 300300, China
Lü M: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Journal Name
Processes
Volume
7
Issue
12
Article Number
E943
Year
2019
Publication Date
2019-12-10
Published Version
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr7120943, Publication Type: Journal Article
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LAPSE:2020.0084
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doi:10.3390/pr7120943
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Jan 19, 2020
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Jan 19, 2020
 
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Original Submitter
Calvin Tsay
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