Knock detection in spark ignition engines based on complementary ensemble improved intrinsic time-scale decomposition (CEIITD) and Bi-spectrum

Abstract

Engine knock limits the thermal efficiency improvement of spark-ignition (SI) engines. Thus, the extract research of the knock characteristics has a great significance for the development of gasoline engines. The research proposes a novel knock detection and diagnosis method in SI engines using the CEIITD (Complementary Ensemble Improved Intrinsic timescale decomposition) and Bi-spectrum algorithm. The CEIITD algorithm is used to extract the knock characteristics. The results show that the CEIITD algorithm can effectively and clearly extract the knock shock characteristics (including light knock) through the vibration signals. A Bi-spectrum analysis can further distinguish between the light knock signal and normal combustion signal. The Bi-spectrum results also show that knock characteristic has a strong non-Gaussian property. At last, the Band pass filter and Improved ITD method were employed to identify the knock characteristics from these cylinder block vibration signals. The comparison result shows that the CEIITD method proposed in this paper is more suitable to detect the knock characteristic.

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

“…COA still uses a greedy algorithm to determine whether the growth of a coyote is allowed. Equation (18) evaluates the growth state of a coyote. In Equation (19), coyotes with better environmental adaptability are retained to participate in the subsequent processes of growth, birth, and death, exclusion from the original group, and acceptance into the new group:…”

“…Time-frequency analysis based on the intrinsic time-scale decomposition can quantitatively describe the relationship between frequency and time, accurately analyzing time-varying signals [10]. On the basis of these advantages, scholars introduced this method from the medical field to the fault diagnosis of mechanical signals [11][12][13][14][15][16][17][18][19][20][21][22]. For example, Lin and Chang published a rolling-bearing fault diagnosis method based on an enhanced kurtosis spectrum and intrinsic time-scale decomposition [11].…”

“…Liu and Zhang et al proposed the use of intrinsic time-scale decomposition for diesel-engine fault diagnosis [17]. Bi and Ma et al proposed to use complete ensemble intrinsic time-scale decomposition and detect gasoline-engine knock [18]. Yu and Liu proposed using sparse coding on the basis of intrinsic time-scale decomposition to diagnose weak bearing faults [19].…”

An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

“…Liu and Zhang et al proposed using intrinsic time-scale decomposition to diagnose diesel-engine faults [19]. Bi and Ma et al proposed detecting gasoline-engine knock using the improved intrinsic time-scale decomposition of complete integration [20]. Yu and Liu proposed sparse coding based on intrinsic time-scale decomposition to diagnose weak bearing faults [21].…”