Wheel Speed Is All You Need: How to Efficiently Detect Automotive Damper Defects Using Frequency Analysis

Dampers are crucial components of the vehicle's suspension to enable safe and comfortable driving. Therefore, defects like an oil leakage or a gas loss need to be detected expeditiously and with high accuracy. In this paper, we present a novel approach that relies solely on wheel speed signals to detect continuous levels of damper degradation. A dedicated 100000km real-world driving data set with multiple relevant damper defects and diverse environmental conditions is used for development and validation. Different vehicle types, routes, vehicle loads, tires, and driving styles are taken into account. Our approach comprises a frequency analysis of the wheel speed signals using the Fast Fourier Transform (FFT). A physical connection between defective dampers and oscillations in the wheel speeds enables a regression model to detect defective dampers. By using the residual sum of a polynomial fit of the FFT data points as a regressor variable, the current level of oil loss is determined. Subsequently, the remaining useful life (RUL) of the damper can be extrapolated. The resulting method is a threefold cascaded regression. In our results, we show a high sensitivity of the damper defect detection to vehicle loads as well as low sensitivity to ambient temperatures and rim sizes. The proposed method achieves a mean absolute error (MAE) of 5.4% oil loss. Future research will focus on efficiently implementing the algorithms onboard the vehicle and sending aggregated data to a remote back end for further analysis.