Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition

分解 特征提取 断层(地质) 模式识别(心理学) 信号(编程语言) 稀疏逼近 特征(语言学) 计算机科学 萃取(化学) 人工智能 数据挖掘 色谱法 化学 地质学 哲学 地震学 有机化学 程序设计语言 语言学
作者
Canyi Du,Fei Jiang,Kang Ding,Li Feng,Feifei Yu
出处
期刊:Shock and Vibration [Hindawi Publishing Corporation]
卷期号:2021 (1) 被引量:6
标识
DOI:10.1155/2021/6650932
摘要

Engine vibration signals are easy to be interfered by other noise, causing feature signals that represent its operating status get submerged and further leading to difficulty in engine fault diagnosis. In addition, most of the signals utilized to verify the extraction method are derived from numerical simulation, which are far away from the real engine signals. To address these problems, this paper combines the priority of signal sparse decomposition and engine finite element model to research a novel feature extraction method for engine misfire diagnosis. Firstly, in order to highlight resonance regions related with impact features, the vibration signal is performed with a high‐pass filter process. Secondly, the dictionary with clear physical meaning is constructed by the unit impulse function, whose parameters are associated with engine system modal characteristics. Afterwards, the signals that indicate the engine operating status are accurately reconstructed by segmental matching pursuit. Finally, a series of precise simulation signals originated from the engine dynamic finite element model, and experimental signals on the automotive engine are used to verify the proposed method’s effectiveness and antinoise performance. Additionally, comparisons with wavelet decomposition further show the proposed method to be more reliable in engine misfire diagnosis.

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