Combustion parameter evaluation of diesel engine via vibration acceleration signal. (October 2022)

• Record Type: Journal Article
• Authors: Zhang, Pan; Gao, Wenzhi; Li, Yong; Wei, Zhaoyi
• Is Part Of: International journal of engine research. Volume 23:Number 10(2022)
• Page Start: 1760
• Page End: 1781
• Publication Date: 2022-10
• Subjects: Combustion parameters -- internal combustion engine -- engine vibration -- in-cylinder pressure -- target regression; Engines -- Periodicals; 629.25
• DOI: 10.1177/14680874211030878
• Languages: English
• ISSNs: 1468-0874

Abstract

Efficient combustion control has increasingly become a quality requirement for automobile manufacturers because of its impact on pollutant and greenhouse gas emissions. In view of this, the management system development of modern internal combustion engines is mainly aimed at combustion control. The real-time detection of in-cylinder pressure characteristic parameters has a considerable significance on the closed-loop combustion control of the internal combustion engine. This paper presents a detection method in which the start of combustion, peak pressure, maximum pressure rise rate, and phase of maximum pressure rise rate are identified through vibration acceleration signal. In order to analyze the relationship between vibration and in-cylinder pressure signal, experimental data are acquired in a diesel engine by implementing various injection strategies and engine operating conditions (speed and load). The results show that the start of combustion can be detected by analyzing its relationship with the peak position of the filtered vibration signal, and the phase of the maximum pressure rise rate can be identified by examining its relationship with the zero-cross position that is adjacent to the right of the peak position. Moreover, the filtered vibration signals are also truncated in the same length and utilized as inputs for algorithms to detect the peak pressure and the maximum pressure rise rate. The algorithms are mainly performed on data compression (or feature extraction) and target regression. Major algorithms, such as one-dimensional convolutional neural network, compression sensing, wavelet decomposition, multilayer perceptron, and support vector machine, are tested. Various experimental results verify that for the test engine the phase detection accuracy of the start of combustion and maximum pressure rise rate is less than 1.7°CA for a 95% prediction interval width. For the detection of the peak pressure and maximum pressure rise rate, the normalized error threshold is set as 0.05, then the accuracies can be not less than 95%.