正反向结合灰自助滤波法在滚动轴承性能可靠性评估中的应用

米月花; 马梦华; 庞晓旭

doi:10.13433/j.cnki.1003-8728.20220133

正反向结合灰自助滤波法在滚动轴承性能可靠性评估中的应用

doi: 10.13433/j.cnki.1003-8728.20220133

1.
郑州商学院信息与机电工程学院, 河南巩义 451200
2.
河南科技大学机电工程学院, 河南洛阳 471003

基金项目:

河南省科技攻关计划项目 202102210083

河南省高等学校重点科研项目 22B510019

详细信息

作者简介:
米月花(1984-), 讲师, 硕士, 研究方向为机电一体化、信号处理, toug147369@163.com

中图分类号: V233.1;TH133.33
计量
- 文章访问数: 706
- HTML全文浏览量: 41
- PDF下载量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-24
- 刊出日期: 2023-10-25

Application of Combined Positive and Reverse Grey-bootstrap Filtering Method in Reliability Evaluation of Rolling Bearing Performance

1.
School of Information and Mechatronics Engineering, Zhengzhou Business University, Gongyi 451200, He′nan, China
2.
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, He′nan, China

摘要

摘要: 基于正反向结合灰自助滤波法提取出有效的轴承性能退化信息，从而精准地计算轴承运行过程中的性能可靠度。分别运用正向灰自助滤波法和反向灰自助滤波法对采集到的滚动轴承振动加速度数据进行降噪，并将其进行融合得到新的数据序列；基于概率密度函数交集法，将滤波前后数据序列的概率密度函数交集面积作为定量评定滤波效果的新指标；选定性能阈值，计算各个数据序列的变异个数、变异概率和性能可靠度，定量分析轴承运行过程中的性能变异程度。两个案例均表明正反向结合灰自助滤波法的效果最好，将其用于评估滚动轴承的性能退化过程是可行的。
- 滚动轴承 /
- 性能可靠性 /
- 正反向结合灰自助滤波法 /
- 概率密度函数交集法 /
- 交集面积
Abstract: Based on the combination of positive and reverse grey-bootstrap filtering methods, the effective bearing performance degradation information can be extracted to accurately calculate the performance reliability of bearings in the operation process. The positive and reverse grey-bootstrap filtering methods were used to denoise the collected vibration acceleration data of rolling bearings respectively, which were fused to obtain the new data series. Based on the probability density function-intersection method, the intersection area of probability density functions was taken as a new index to quantitatively evaluate the filtering effect for the data series before and after filtering. The performance threshold was selected to calculate the variation number, variation probability and performance reliability of data series. The variation degree of bearing performance in the operation process was analyzed quantitatively. Both cases show that the combined positive and reverse grey-bootstrap filtering method has the best filtering effect, and It is feasible to use it to evaluate the performance degradation process of rolling bearings.
- rolling bearing /
- performance reliability /
- combined positive and reverse grey-bootstrap filtering method /
- probability density function-intersection method /
- intersection area

HTML全文

图 1 滚动轴承振动加速度数据序列

Figure 1. Vibration acceleration data sequence of rolling bearing

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图 2 第1数据序列的滤波结果

Figure 2. Filter results of the first data sequence

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图 3 第2数据序列的滤波结果

Figure 3. Filter results of the second data sequence

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图 4 第3数据序列的滤波结果

Figure 4. Filter results of the third data sequence

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图 5 第4数据序列的滤波结果

Figure 5. Filter results of the fourth data sequence

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图 6 滤波前后第1数据序列的概率密度函数

Figure 6. Probability density functions of the first data sequence before and after filtering

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图 7 振动性能可靠度与磨损直径的关系

Figure 7. Relationship between vibration performance reliability and wear diameter

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图 8 滚动轴承振动加速度均方根序列

Figure 8. Root-mean-square sequence of rolling bearing vibration acceleration

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图 9 各种滤波方法的结果

Figure 9. Results on various filtering methods

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图 10 滤波前后的概率密度函数

Figure 10. Probability density functions before and after filtering

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图 11 轴承振动性能可靠度

Figure 11. Vibration performance reliability of bearings

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表 1 滤波前后各个样本的拉格朗日乘子

Table 1. Lagrange multipliers of samples before and after filtering

样本	拉格朗日乘子
样本	a₁	a₂	a₃	a₄	a₅	a₆
滤波前	1.81	0.32	-0.56	-0.04	-0.04	-0.01
正向滤波	2.04	0.12	-0.71	-0.01	-0.01	-0.01
正反向结合	2.04	0.11	-0.69	0.00	-0.01	-0.01

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表 2 滤波前后各个概率密度函数的重合面积(案例1)

Table 2. Overlapped areas of probability density functions before and after filtering(case 1)

样本	交点		重合面积
样本	横坐标1	横坐标2	重合面积
正向滤波	-0.0471	0.0606	0.9190
正反向结合	-0.047 3	0.061 0	0.929 6
滚动均值滤波	-0.041 5	0.060 2	0.833 7

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表 3 各个振动序列的变异个数、变异概率可靠度(案例1)

Table 3. Variation number, variation probability and reliability of different vibration sequences(case 1)

数据序列号	变异个数	变异概率	可靠度/%
1	0	0	100
2	8	0.005	99.50
3	40	0.025	97.53
4	565	0.353	70.25

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表 4 各种滤波方法的拉格朗日乘子

Table 4. Lagrange multipliers of various filtering methods

样本	拉格朗日乘子
样本	a₁	a₂	a₃	a₄	a₅	a₆
滤波前	-3.57	3.86	-6.41	-5.94	0.78	0.76
正向滤波	-5.25	5.99	-1.26	-4.43	0.14	0.51
正反向结合	-5.30	5.97	-1.13	-4.37	0.12	0.50

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表 5 滤波前后各个概率密度函数的重合面积(案例2)

Table 5. Overlapped areas of probability density functions before and after filtering (Case 2)

方法	交点				重合面积
方法	横坐标1	横坐标2	横坐标3	横坐标4	重合面积
正向滤波	1.582 5	2.995 1	9.841 8	13.489 6	0.900 2
正反向结合	1.633 3	3.020 9	9.881 6	13.533 0	0.915 7
滚动均值滤波	2.242 2	3.747 6	10.267 4	13.755 7	0.863 8

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表 6 各个振动序列的变异个数、变异概率可靠度(案例2)

Table 6. Variation number, variation probability and reliability of different vibration sequences (Case 2)

数据序列号	变异个数	变异概率	可靠度/%
1	0	0	100
2	0	0	100
3	0	0	100
4	0	0	100
5	0	0	100
6	0	0	100
7	0	0	100
8	0	0	100
9	80	0.13	87.91
10	621	1.00	36.79

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