Data-Driven Fault Detection of Automotive Engine Mounts Using Onboard Vibration Signals

With the increasing rate at which new technologies are developed and integrated into modern vehicles, their complexity and demands on reliability have grown. Hence, potential downtimes are directly correlated with high costs for both customers and manufacturers, making the need to ensure operational stability evident. Being the most crucial part of an automotive vehicle, the integrity of the drivetrain and its components have to be assured. Traditionally, this was achieved by increased loads during the design phase or regular service intervals. As these preventive measures lack the ability to assess or detect the imminent occurrence of component faults, aggregating damages and connected costs still remain inevitable, especially as direct data acquisition for such components is not possible. Due to the recent advancement of onboard functionalities of modern cars, constant and extensive sensory data is generated throughout each part of the vehicle, enabling the proactive monitoring of its condition. Caused by the mechanical and thermal loads during operation, altered mechanical properties of damaged components such as engine mounts can influence the overall vibrational behavior of the vehicle. By analyzing statistical features such as Root Mean Square, Kurtosis, and Crest Factor of the vibrational signals generated by the car’s onboard sensors, this study aims to investigate them for the fault detection of engine mounts. Through the comparison of healthy and unhealthy states across different vehicles in the initial phase of engine ignition, the results show that only the Root Mean Square was able to consistently differentiate the health states, suggesting the higher energy dissipation within the fault-induced state as main indicator for detection