Extended kernel density estimation for mining electric shovel torque load spectrum analysis

This paper proposes a method for compiling the torque load spectrum of an electric shovel motor output shaft to investigate torque variation under extreme conditions, providing a foundation for structural optimization. Using the WK-35 mining electric shovel as the research subject, extended kernel density estimation is employed to simulate extreme torque events during shovel operation and reconstruct the load-time history. To address the limitation of data acquisition software in capturing extreme torque data, a load extrapolation method based on Markov Chain Monte Carlo kernel density estimation is proposed. The Rainflow method records torque frequency information, while Epanechnikov kernel density extrapolation implements torque extrapolation. Based on the extrapolated results, a one-dimensional amplitude load spectrum for torque is presented. Results demonstrate that torque extrapolated via extended kernel density estimation simulates a wider torque variation range and enhances the sample size of high-torque data. The extrapolated load spectrum provides a basis for accelerated life testing of the motor output shaft.