Bibliographic Details
| Title: |
Performance degradation and maintenance optimization strategy of rolling bearings based on data fusion and adaptive partition. |
| Alternate Title: |
基于数据融合和自适应划分的滚动轴承性能退化与维护优化策略 |
| Authors: |
Cheng, Hong-Chuan1,2,3 (AUTHOR), Li, Xin-Hai1 (AUTHOR), Ma, Guo-Hui1 (AUTHOR), Cui, Yu3 (AUTHOR), Shang, Zhi-Wu4 (AUTHOR), Shi, Xia-Fei5 (AUTHOR) shixiafei@tiangong.edu.cn |
| Source: |
Acta Mechanica Sinica. Feb2026, Vol. 42 Issue 2, p1-17. 17p. |
| Abstract (English): |
The traditional method of performance degradation prediction and maintenance of rolling bearings only considers a single sensor signal, which makes it difficult to automatically partition degradation stages and prone to over-detection. A new method of performance degradation evaluation and maintenance of rolling bearings based on data-level fusion, adaptive health state partitioning, and state maintenance is proposed. Firstly, considering the degradation and impact in the process of bearing deterioration, the multi-sensor signals are dynamically weighted to achieve data-level fusion. Secondly, a bearing health index was established based on fast spectral correlation, Wasserstein distance, and linear rectification techniques. On this basis, by combining the Bayesian information criterion and the elbow rule, the precise division of rolling bearing health state is realized through hidden Markov model regression. Then, random forest was used to classify and predict the data to verify the validity of the proposed data fusion method and health indicator. Finally, condition-based maintenance strategy based on the fourth moment, stress-strength interference model, and Gamma process is proposed to avoid excessive detection and reduce maintenance costs. Through accelerated degradation experiments and field validation tests on the rolling bearing test data set of Xi’an Jiaotong University and FEMTO (PRONOSTIA), the accuracy and superiority of the proposed method in the prediction and maintenance of bearing health state are verified. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
摘要: 针对滚动轴承性能退化预测与维护的传统方法只考虑单一传感器信号, 难以自适应划分退化阶段且易出现过度检测的问题, 本 文提出了一种基于数据级融合、健康状态自适应划分和状态维修的滚动轴承性能退化评估和维护新方法. 首先, 考虑到轴承劣化过程 中的退化与冲击作用, 对多传感器信号进行动态加权实现数据级融合. 其次, 基于快速谱相关、Wasserstein距离和线性整流技术建立了 一种能够表征轴承状态的健康指标. 在此基础上, 通过隐马尔可夫模型回归结合贝叶斯信息准则和肘部法则, 实现滚动轴承健康状态 的精确划分. 再次, 利用随机森林对数据进行分类预测, 验证所提数据融合方法和健康指标的有效性. 最后, 以单位时间维修费用为优 化目标, 提出了基于四阶矩、应力-强度干涉模型和Gamma过程的状态维修策略, 避免过度检测, 降低维护费用. 通过对在西安交通大 学数据集和FEMTO(PRONOSTIA)轴承加速寿命数据集上的实验, 验证所提方法在轴承健康状态预测与维护中的准确性和优越性. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
