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

A novel method for gear gravimetric wear prediction based on improved particle swarm optimization... 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/approachFirstly, 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.FindingsIn 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/valueFinally, 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations Emerald Publishing

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

Chen, Cheng; Wang, Honghua
Engineering Computations , Volume 38 (5): 24 – Jun 30, 2021
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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0264-4401
DOI
10.1108/ec-03-2020-0177
Publisher site
See Article on Publisher Site

Abstract

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/approachFirstly, 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.FindingsIn 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/valueFinally, 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.

Journal

Engineering Computations – Emerald Publishing

Published: Jun 30, 2021

Keywords: Gear gravimetric wear loss prediction; Improved PSO; Most probable estimation method; Non-stationary random process; Probability distribution fitting

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