国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类
关键词搜索：
| 标 题： |
A Data-Driven Fault Diagnosis Method Using Modified Health Index and Deep Neural Networks of a Rolling Bearing
基于改进健康指数与深度神经网络的滚动轴承数据驱动故障诊断方法 |
| 作 者： |
Lin, Muyangzi
Zhou, Jie
Pan, Yunjie
Shan, Miyuan |
| 关键词： |
modified ensemble empirical mode decomposition (MEEMD) [改进型集合经验模态分解（MEEMD）]
adjustment Mahalanobis-Taguchi system (AMTS) [ 调整马哈拉诺比斯-田口系统（AMTS）]
modified health index (MHI) [ 改良健康指数（MHI）]
deep neural networks (DNN) [ 深度神经网络（DNN）]
artificial intelligence [ 人工智能]
big data and analytics [ 大数据与分析]
data-driven engineering [ 数据驱动工程]
EMPIRICAL MODE DECOMPO [ 经验模式分解] |
| 出版年： |
2022 |
| 期刊名： |
Journal of Computing and Information Science in Engineering
计算与信息科学工程杂志 |
| 年卷期： |
2022, vol.22, no.2 |
| 摘 要： |
To improve fault diagnosis accuracy, a data-driven fault diagnosis model based on the adjustment Mahalanobis-Taguchi system (AMTS) was proposed. This model can analyze and identify the characteristics of vibration signals by using degradation monitoring as the classifier to capture and recognize the faults of the product more accurately. To achieve this goal, we first used the modified ensemble empirical mode decomposition (MEEMD) scalar index to capture the bearing condition; then, by using the key intrinsic mode function (IMF) extracted by AMTS as the input of classifier, the optimized properties of bearing is decomposed and extracted effectively. Next, to improve the accuracy of the fault diagnosis, we tested different modes, employing the modified health index (MHI), which is designed to overcome the shortcomings of the proposed health index as a classifier in a single fault mode and the deep neural networks (DNNs) as a classifier in a multifault mode. To evaluate the effectiveness of our model, the Case Western Reserve University (CWRU) bearing data were used for verification. Results indicated a strong robustness with 99.16% and 1.09s, 99.86% and 6.61s fault diagnosis accuracy in different data modes. Furthermore, we argue that this data-driven fault diagnosis obviously lowers the maintenance cost of complex systems by significantly reducing the inspection frequency and improves future safety and reliability.
为提高故障诊断的准确性，提出了一种基于调整马哈拉诺比斯-田口系统（AMTS）的数据驱动型故障诊断模型。该模型利用劣化监测作为分类器，对振动信号进行分析和识别，从而更准确地捕捉和识别产品的故障特征。为实现这一目标，我们首先采用改进的集合经验模态分解（MEEMD）标量指数来获取轴承状态； 随后，通过将AMTS提取的关键固有模式函数（IMF）作为分类器的输入，有效分解并提取了轴承的优化特性。为了提高故障诊断的准确性，我们测试了不同的模式，并采用改进型健康指数（MHI），该指数旨在克服单一故障模式下所提健康指数作为分类器的不足，以及多故障模式下深度神经网络（DNNs）作为分类器的局限性。为评估模型的有效性，我们使用了Case Western Reserve大学（CWRU）的轴承数据进行验证。结果表明，在不同数据模式下，模型具有较强的鲁棒性，故障诊断准确率分别为99.16%和1.09秒、99.86%和6.61秒。此外，我们认为这种数据驱动的故障诊断方法显著降低了复杂系统的维护成本，通过大幅减少检测频率，提升了系统的未来安全性和可靠性。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
京公网安备 11010202009788