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

Abstract

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.

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

“…There is a long history of works focused on misfire fault detection in internal combustion engines. Many approaches utilize vibration analysis [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ] and physics-based estimations such as with acceleration, torque, and speed [ 30 , 31 , 32 , 33 ].…”

A useful method of signal de-noising

“…Singular value reflects the energy distribution of useful signals and noise in the signal. Singular value decomposition (SVD) can remove the noise, extract the features of the signal and analyze the components of the signal [5,6]. However, the SVD denoising method is difficult to separate and extract the feature components at a low signal-to-noise ratio (SNR).…”