Fault Diagnosis Method for High-speed Train Gearbox Bearing Based on Improved VMD and Temperature-vibration Feature Fusion

Objective Existing methods for high-speed train gearbox bearing monitoring and diagnosis in China often rely solely on temperature or vibration data. Rely solely on a single temperature data point may result in missing early fault information of key components, while only vibration data may struggle to support identification of faults under complex coupling conditions. Therefore, it is necessary to combine temperature and vibration data to develop a fault diagnosis method for gearbox bearings with temperature-vibration features. Method To determine the decomposition parameters of VMD (variational mode decomposition) method, a weighted kurtosis coefficient indicator is introduced. Combining LMD (local mean decomposition) and VMD methods, a new approach for processing raw vibration data and extracting fault features is proposed. Based on the improved VMD method, LLE (locally linear embedding) feature dimensionality reduction method, and BP (back-propagation) neural network, a method for temperature-vibration fe