Multi-Method Approach for car tailgate electric telescopic rod Noise: Detection, Characterization, and Prediction

As the automotive industry moves towards greater intelligence, electric tailgate systems have seen widespread adoption, featuring remote control, obstacle detection, and intelligent opening functions that significantly enhance the user experience. The electric telescopic rod, as a key actuator, has drawn attention for its structural and transmission design. However, studies have shown that during actual operation, various noise issues arise with electric telescopic rods, affecting the sound quality and smoothness of the tailgate's opening and closing. This paper presents a noise detection and analysis study based on a bench test platform for a specific model of electric telescopic rod. Using a microphone to capture noise signals, three main types of noise were identified: high-frequency electromagnetic noise, low-frequency impact noise, and periodic structural noise. The study accurately detects high-frequency electromagnetic noise using wavelet packet energy feature extraction techniques and identifies and localizes low-frequency impact noise using a sliding window dynamic threshold method. It also distinguishes this noise from test bench noise through frequency domain characteristics. Additionally, autocorrelation analysis was employed to detect and evaluate periodic noise. The experimental results demonstrate that these methods effectively identify and localize various noise sources, providing data support for future noise source analysis.