齿轮性能退化评估的时序重构模型

张龙; 黄婧; 吴荣真; 宋成洋; 王朝兵

doi:10.13433/j.cnki.1003-8728.20200539

齿轮性能退化评估的时序重构模型

doi: 10.13433/j.cnki.1003-8728.20200539

张龙^1,,
黄婧¹,
吴荣真¹,
宋成洋¹,
王朝兵^{1, 2}

1.
华东交通大学机电与车辆工程学院，南昌　330013
2.
中车戚墅堰机车有限公司，江苏常州　213011

基金项目: 国家自然科学基金项目（51665013，51865130）与江西省研究生创新资金项目（YC2019-S243）

详细信息

作者简介:
张龙（1980−），副教授，博士，研究方向为故障诊断、工程信号处理及智能算法，longzh@126.com

中图分类号: TH212;TH213.3
计量
- 文章访问数: 149
- HTML全文浏览量: 62
- PDF下载量: 8
- 被引次数: 0
出版历程
- 收稿日期: 2021-01-06
- 网络出版日期: 2023-02-16
- 刊出日期: 2022-12-05

Performance Degradation Assessment of Gears based on AR Model and Dictionary Learning

1.
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
2.
CRRC QISHUYAN Co., Ltd., Changzhou 213011, Jiangsu, China

摘要

摘要: 齿轮性能退化评估是预诊断的提前和基础，针对概率相似度量评估方法存在模型复杂，容易过早饱和等现象，提出一种基于AR (Autoregressive model)模型和字典学习的齿轮性能退化评估的重构模型方法，其中AR模型用于提取齿轮振动信号的状态特征，字典学习通过正常状态下构建的字典模型(Dictionary learning, DL)对测试样本进行AR模型系数重构。首先提取正常运行状态下振动信号的AR模型系数构建过完备字典模型，然后将待测信号的AR系数作为特征向量输入字典模型中得到重构后的AR模型系数。最后由原始AR系数和重构AR系数分别构造自回归模型，并各自完成对待测信号的时序建模，将两自回归模型所得残差序列的均方根误差作为性能劣化程度指标。全寿命疲劳实验数据分析结果表明，与传统时域指标相比该方法对早期故障更敏感且具有与齿轮故障发展趋势一致性更好等优点。
- AR模型 /
- 字典学习 /
- 齿轮 /
- 性能退化评估 /
- 重构模型
Abstract: The performance degradation assessment (PDA) is the advance and foundation of gear fault pre-diagnosis. Aiming at the problems that similarity-based methods are of complex models and time consuming, a reconstruction model of gear PDA based on Autoregressive Model (AR) and Dictionary Learning (DL) is proposed. The coefficients of AR model serve as feature vectors to depict gear performance states and DL model is used to reconstruct AR coefficients. Firstly, the over-complete dictionary model are constructed with the AR coefficients of vibration signals under normal operation, and then the AR coefficients of the signals under consideration are input into the DL model as feature vectors to obtain the reconstructed feature vectors. By inputting the signal at hand into the two autoregressive models composed of original AR coefficients and reconstructed AR coefficients respectively, the corresponding residual sequences are obtained. Finally, Mean Squared Error (MSE) is introduced to evaluate the two residual sequences as performance degradation index. Run-to-failure data set from gear are processed to demonstrate the advantages of the method in terms of the trendability, consistency and sensitivity of early failure.
- ARmodel /
- dictionary learning /
- gear /
- performance degradation assessment /
- restructuring model

HTML全文

图 1 齿轮性能退化评估流程图

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图 2 齿轮故障程度

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图 3 齿轮全寿命时域图

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图 4 部分信号包络谱

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图 5 齿轮时域指标图

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图 6 循环位移

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图 7 BIC值变化曲线

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图 8 字典原子个数K值不同时的RMSE

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图 9 稀疏度L不同时的RMSE

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图 10 迭代次数I不同时的RMSE

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图 11 前35个样本训练的齿轮性能退化曲线

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图 12 前40个样本训练的齿轮性能退化曲线

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图 13 前50个样本训练的齿轮性能退化曲线

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图 14 基于LLP和耦合隐马尔可夫模型的齿轮退化评估结果

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表 1 圆柱齿轮齿轮箱参数

名称	主动轮（测试齿轮）	从动轮
转速/（r·min⁻¹）	1000	1000
载荷/（daN·m）	200	200
齿数	20	21
转频/Hz	16.67	15.86
齿宽/m	0.015	0.03
模数/m	0.01	0.01
压力角/（°）	20	20

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表 2 齿轮箱疲劳试验每日停机观测结果

观测日期/d	观测结果
1	无故障
2	无故障
3	无故障
4	无故障
5	无故障
6	第二齿产生剥落
7	第二齿剥落程度无恶化
8	第二齿无恶化；第十六齿产生早期剥落
9	第十六齿剥落继续增大
10	同上
11	同上
12	第十六齿的剥落面积覆盖到整个齿宽

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