A novel method for gear gravimetric wear prediction based on improved particle swarm optimization and non-stationary random process probability distribution fitting

Abstract

Purpose

Stimulated by previous reference, which proposed making straight line of regression to test gear gravimetric wear loss sequence distribution, this paper aims to propose using straight line of regression to fit gear gravimetric wear loss sequence based on stationary random process suppose. Faced to that the stationary random sequence suppose had not been proved by previous reference, and that prediction did not present high precision, this paper proposes a method of fitting non-stationary random process probability distribution function.

Design/methodology/approach

Firstly, this paper proposes using weighted sum of Gauss items to fit zero-step approximate probability density. Secondly, for the beginning, this paper uses the method with few Gauss items under low precision. With the amount of points increasing, this paper uses more Gauss items under higher precision, and some Gauss items and some former points are deleted under precision condition. Thirdly, for particle swarm optimization with constraint problem, this paper proposed improved method, and the stop condition is under precision condition.

Findings

In experiment data analysis section, gear wear loss prediction is done by the method proposed by this paper. Compared with the method based on the stationary random sequence suppose by prediction relative error, the method proposed by this paper lowers the relative error whose absolute values are more than 5%, except when the current point sequence number is 2, and retains the relative error, whose absolute values are lower than 5%, still lower than 5%.

Originality/value

Finally, the method proposed by this paper based on non-stationary random sequence suppose is proved to be the better method in gear gravimetric wear loss prediction.

Citation

Chen, C. and Wang, H. (2021), "A novel method for gear gravimetric wear prediction based on improved particle swarm optimization and non-stationary random process probability distribution fitting", Engineering Computations, Vol. 38 No. 5, pp. 2024-2047. https://doi.org/10.1108/EC-03-2020-0177