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采用统计方法利用发动机缸体振动对多燃料发动机进行爆震检测。

Engine knock detection for a multifuel engine using engine block vibration with statistical approach.

作者信息

Ismail Muammar Mukhsin, Fawzi Mas, Taweekun Juntakan, Leevijit Theerayut

机构信息

Centre for Energy and Industrial Environment Studies (CEIES), Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor 86400 Malaysia.

Energy Technology Program, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand.

出版信息

MethodsX. 2021 Nov 18;8:101583. doi: 10.1016/j.mex.2021.101583. eCollection 2021.

DOI:10.1016/j.mex.2021.101583
PMID:35004216
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8720894/
Abstract

Engine knock is an obstacle for maximizing CNG fuel utilization on a Diesel-CNG Dual Fuel engine. Prolong experience of this phenomenon may lead to severe engine damage. The low intensity of this phenomenon is difficult to recognize due to other noises from the engine. Thus, improper engine tuning techniques may make this phenomenon unnoticeable until the engine damages. Knock phenomena on such engines may not be detected in the combustion analysis graph. Its random occurrence in consecutive engine cycles makes it difficult to be seen using visual data. This knowledge gap, if closed, can lead to an opportunity for knock avoidance on the multifuel engine. This work proposed a method to quantify the knock occurrence based on engine block vibration using a single piezoelectric knock sensor. The knock occurrence was detected by comparing the calculated knock index with the knock threshold, determined using a statistical three-sigma rule analysis. This method can index the knock intensity, detect the engine knock occurrence, and visualize the knock phenomenon.•This paper describes an alternative engine knock detection technique based on engine block vibration.•This method proposes the knock threshold determination based on statistical three-sigma rule analysis.•This method is capable of visualizing the knock phenomenon in consecutive and at each engine cycle.

摘要

发动机爆震是柴油-压缩天然气双燃料发动机最大限度利用压缩天然气燃料的一个障碍。长期经历这种现象可能会导致发动机严重损坏。由于发动机的其他噪音,这种现象的低强度很难识别。因此,不正确的发动机调校技术可能会使这种现象在发动机损坏之前都不被察觉。这种发动机上的爆震现象在燃烧分析图中可能检测不到。它在连续的发动机循环中随机出现,使得通过视觉数据很难看到。如果填补了这个知识空白,就有可能在多燃料发动机上避免爆震。这项工作提出了一种基于发动机缸体振动,使用单个压电爆震传感器来量化爆震发生情况的方法。通过将计算出的爆震指数与使用统计三西格玛规则分析确定的爆震阈值进行比较来检测爆震的发生。该方法可以对爆震强度进行索引,检测发动机爆震的发生,并将爆震现象可视化。

• 本文描述了一种基于发动机缸体振动的替代发动机爆震检测技术。

• 该方法提出基于统计三西格玛规则分析来确定爆震阈值。

• 该方法能够在连续的每个发动机循环中可视化爆震现象。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/a4197ac4cc8d/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/7bcbca6facf7/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/12a88558a8a8/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/9c1e7d0c9f12/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/29c88532dc57/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/32c1687bacef/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/c5fec28cd53e/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/6b0e6f353636/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/94860160e7e9/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/b2e44be8023c/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/874a2e04e0cc/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/a4197ac4cc8d/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/7bcbca6facf7/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/12a88558a8a8/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/9c1e7d0c9f12/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/29c88532dc57/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/32c1687bacef/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/c5fec28cd53e/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/6b0e6f353636/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/94860160e7e9/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/b2e44be8023c/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/874a2e04e0cc/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3684/8720894/a4197ac4cc8d/gr10.jpg

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