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

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 time-scale 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

Knock Detection in Spark Ignition Engines Base on Complementary Ensemble Empirical Mode Decomposition-Hilbert Transform

In spark ignition engines, knock onset limits the maximum spark advance. An inaccurate identification of this limit penalises the fuel conversion efficiency. Thus knock feature extraction is the key of closed-loop control of ignition in spark ignition engine. This paper reports an investigation of knock detection in spark ignition (SI) engines using CEEMD-Hilbert transform based on the engine cylinder pressure signals and engine cylinder block vibration signals. Complementary Ensemble Empirical Mode Decomposition (CEEMD) was used to decompose the signal and detect knock characteristic. Hilbert transform was used to analyze the frequency information of knock characteristic. The result shows that, for both of cylinder pressure signals and vibration signals, the CEEMD algorithm could extract the knock characteristic, and the Hilbert transform result shows that the energy of knock impact areas has the phenomenon of frequency concentration in both cylinder pressure signal and cylinder block vibration signal. At last, the knock window is then determined, based on which a new knock intensity evaluation factor K is propose, and it can accurately distinguish between heavy knock, light knock, and normal combustion three states

Measuring Domain Shift in Vibration Signals to Improve Cross-Domain Diagnosis of Piston Aero Engine Faults

Transfer learning is an effective approach to address the decline in generalizability of intelligent fault diagnosis methods. However, there has been a persistent lack of comprehensive and effective metrics for assessing the transferability of cross-domain data, making it challenging to answer the fundamental question in transfer learning: “When to transfer”. This study proposes a novel hybrid transferability metric (HTM) based on weighted correlation-diversity shift. The metric introduces a correlation shift measurement based on sparse principal component analysis, effectively quantifying distribution differences in domain-invariant features based on the sparse representation theory. It also designs a diversity shift measurement based on label space differences, addressing the previously overlooked impact of label variation on transferability. The proposed transferability metric is validated on four types of cross-domain diagnosis tasks involving piston aero engines. The results show that in diagnostic scenarios involving both supervised transfer learning and extreme class imbalance problems, HTM accurately predicted the transferability of the target tasks, which aligned with the actual diagnostic accuracy trends. It provides a feasible method for predicting and evaluating the applicability of transfer learning methods in real-world scenarios

Reduction of low frequency vibration of truck driver and seating system through system parameter identification, sensitivity analysis and active control

This paper aims to develop an 5-degree-of-freedom driver and seating system model for optimal vibration control. A new method for identification of the driver seating system parameters from experimental vibration measurement has been developed. The parameter sensitivity analysis has been conducted considering the random excitation frequency and system parameter uncertainty. The most and least sensitive system parameters for the transmissibility ratio have been identified. The optimised PID controllers have been developed to reduce the driver\u27s body vibration