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.

中文翻译：

---

基于改进粒子群优化和非平稳随机过程概率分布拟合的齿轮重力磨损预测新方法

目的

受前人的启发，提出用回归直线来检验齿轮重量磨损损失序列分布，本文旨在提出基于平稳随机过程假设，用回归直线拟合齿轮重量磨损损失序列。针对平稳随机序列假设没有被前人证明，预测精度不高，提出了一种拟合非平稳随机过程概率分布函数的方法。

设计/方法/方法

首先，本文提出使用高斯项的加权和来拟合零步近似概率密度。其次，本文首先采用低精度下高斯项少的方法。随着点数的增加，本文在精度更高的情况下使用了更多的高斯项，在精度条件下删除了一些高斯项和一些以前的点。第三，针对有约束问题的粒子群优化，提出了改进方法，停止条件为精度条件。

发现

在实验数据分析部分，采用本文提出的方法进行齿轮磨损损失预测。与基于平稳随机序列的预测相对误差假设的方法相比，本文提出的方法降低了绝对值大于5%的相对误差，除了当前点序列号为2时，保留了相对误差，其绝对值低于 5%，仍低于 5%。

原创性/价值

最后，本文提出的基于非平稳随机序列假设的方法被证明是齿轮重量磨损损失预测的较好方法。