基于MSET重构模型整体优化的轴承性能退化评估方法

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摘要针对传统单域特征指标无法充分表征轴承性能退化的状态信息，而基于多域高维特征向量的重构评估模型存在信息冗余且易受到不一致优化目标的影响而导致模型次优性能的问题，提出一种基于多元状态估计(Multivariate State Estimation Technique, MSET)重构模型整体优化的轴承性能退化评估方法。首先，提取轴承振动信号的多个时域和频域特征、自回归模型系数和三层小波包Renyi熵组成高维多域特征向量，同时将健康状态的高维特征向量构建MSET重构模型的历史记忆矩阵；然后利用遗传算法(Genetic Algorithm, GA)对轴承高维特征向量和MSET模型中的历史记忆矩阵进行同步联合优化，从而实现特征优选和重构评估模型的整体自适应优化，进一步提高降维后特征向量与重构模型的匹配性；最后利用余弦相似度作为故障程度指标构建轴承性能退化评估曲线。西安交大-昇阳科技(XJTU-SY)联合实验室滚动轴承疲劳试验全寿命数据分析结果表明，本文所提方法具有一定的有效性和可靠性。

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	张龙
	刘杨远
	吴荣真
	王良
	承志恒
	颜秋宏

关键词 ：多域特征向量, 多元状态估计重构模型, 历史记忆矩阵, 遗传算法, 同步联合优化

Abstract：Aiming at the problem that the traditional single domain feature index can not fully represent the state information of bearing performance degradation, and the reconstruction evaluation model based on multi-domain high-dimensional feature vector has information redundancy and is vulnerable to the influence of inconsistent optimization objectives, which leads to the suboptimal performance of the model, a bearing performance degradation evaluation method based on the global optimization of Multivariate State Estimation Technique(MSET) reconfiguration model is proposed. Firstly, several time and frequency domain features of bearing vibration signals, autoregressive model coefficients and three-layer wavelet packet Renyi entropy are extracted to form high-dimensional multi-domain feature vectors. At the same time, the high-dimensional feature vectors of health state are used to construct the historical observation matrix of MSET model. And then Genetic Algorithm (GA) is used to synchronously optimize the high-dimensional feature vector of bearing and the history memory matrix of MSET model, so as to realize the overall adaptive optimization of feature selection and reconstruction evaluation model, and further improve the matching between feature vector and reconstruction model after dimensionality reduction. Finally, cosine similarity is used as the fault degree index to construct the bearing performance degradation evaluation curve. The analysis of the whole life data of bearing fatigue test provided by Xi'an Jiaotong University-Shenyang Science and Technology Joint Laboratory (XJTU-SY) show that the method proposed in this paper is effective and reliable.

Key words： multi-domain feature vector reconstruction model of Multivariate State Estimation Technique historical memory matrix Genetic Algorithm synchronous joint optimization

收稿日期: 2022-05-19 出版日期: 2023-08-28

引用本文:

张龙，刘杨远，吴荣真，王良，承志恒，颜秋宏. 基于MSET重构模型整体优化的轴承性能退化评估方法[J]. 振动与冲击, 2023, 42(16): 251-261.
ZHANG Long, LIU Yangyuan, WU Rongzhen, WANG Liang, CHENG Zhiheng, YAN Qiuhong. Evaluation of bearing performance degradation based on global optimization of an MSET reconstruction model. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(16): 251-261.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I16/251

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