Determination of Weak Knock Characteristics for Two-Stroke Spark Ignition UAV Engines Based on Mallat Decomposition Algorithm

Jing Sheng,Yuping Zeng,Rui Liu,Guoman Liu,Emiliano Mucchi

doi:10.1155/2021/1250327

Abstract

Two-stroke spark ignition (SI) unmanned aerial vehicle (UAV) engines do not allow heavy knock and require a certain knock safety margin. However, weak knock can help the engine increase power output and reduce fuel consumption. To accurately extract the knock characteristics of engine vibration signals under the condition of weak knock, a signal feature extraction method based on the Mallat decomposition algorithm was proposed. Mallat decomposition algorithm can decompose the signal into two parts: a low-frequency signal and a high-frequency noise signal. The decomposed high-frequency noise is eliminated, and the low-frequency signal is retained as the characteristic domain signal. Simulation results show the effectiveness of the proposed algorithm. The engine vibration signal of a two-stroke SI UAV engine was decomposed into the low-frequency signal and the high-frequency signal by the Mallat decomposition algorithm. The low-frequency signal is taken as the knock characteristic domain signal component, and the wavelet packet energy method is used to verify the correctness of the obtained signal component. The relative energy parameter is calculated by using the knock characteristic domain signal component, which can be used as the determination index of knock intensity. This method provides a reference for the weak knock control of two-stroke SI UAV engines.

Highlights

Fuel-powered unmanned aerial vehicle (UAV) have been widely applied in agricultural, forestry, plant operations, and military due to their simple structure, easy control, steady running, and landform independence [1]
Signal transform technology is a useful tool for knock feature extraction methods, such as short-time Fourier transform (STFT [13]), Wigner–Ville distribution (WVD [14]), and wavelet transform [15,16,17], which have been applied to analyze engine vibration signals for knock detection in the past decades
The Mallat decomposition algorithm is used to separate the engine vibration signals of the twostroke spark ignition (SI) UAV engine, and the knock characteristic domain signal component is extracted effectively. is component can be used to calculate the relative energy parameter as an index to evaluate the knock intensity, which lays a foundation for knock identification

Summary

Introduction

Fuel-powered UAVs have been widely applied in agricultural, forestry, plant operations, and military due to their simple structure, easy control, steady running, and landform independence [1]. E pressure shock waves generated by high-speed knock combustion in the cylinder reflect and repeatedly impact the cylinder wall, causing forced vibration and high-frequency noise. E indirect knock detection method uses the vibration acceleration sensor to measure the response of the engine body under knock excitation and processes the measured vibration signals to highlight the knock characteristics. Signal transform technology is a useful tool for knock feature extraction methods, such as short-time Fourier transform (STFT [13]), Wigner–Ville distribution (WVD [14]), and wavelet transform [15,16,17], which have been applied to analyze engine vibration signals for knock detection in the past decades. The Mallat decomposition algorithm is used to separate the engine vibration signals of the twostroke SI UAV engine, and the knock characteristic domain signal component is extracted effectively. The Mallat decomposition algorithm is used to separate the engine vibration signals of the twostroke SI UAV engine, and the knock characteristic domain signal component is extracted effectively. is component can be used to calculate the relative energy parameter as an index to evaluate the knock intensity, which lays a foundation for knock identification

Method

Experiment Setup

Simulation Experiment

15 X: 3125 Y

Conclusion