基于灰狼优化器的自适应核字典学习轴承智能故障诊断方法

IF 1.7 4区 工程技术 Q3 ENGINEERING, INDUSTRIAL
Tao Yang, Xin Zhang, Jiaxu Wang, Yu Jin, Zhiyuan Gong, Lei Wang
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引用次数: 0

摘要

提出了一种用于轴承智能故障诊断的自适应核字典学习方法。核KKSVD (Kernel KSVD)是一种很好的字典学习方法,具有处理非线性信号的能力。然而,核参数和稀疏级别的选择是一个关键问题,因为这些参数分别决定了高维核空间的形式和KKSVD学习合适的原子信息来表示样本的能力。因此,通过预先指定参数值很难实现KKSVD的最大性能。为了解决这个问题,在KKSVD中引入了一种先进的元启发式算法,即灰狼优化器(GWO)。具体而言,首先设计目标函数,将待优化的参数参与到轴承训练集的KKSVD学习过程中,然后将其应用到轴承验证集的测试中,得到分类结果。将分类精度反馈给GWO算法,GWO算法迭代更新参数,输出最优参数。针对轴承故障诊断中常见的两种情况,即强噪声样本和不平衡样本,分别进行了两个案例研究。分析结果表明,该方法能够有效地自适应获取最优参数,提高KKSVD的性能。此外,该方法在诊断准确性和鲁棒性方面优于几种最先进的字典方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive kernel dictionary learning method based on grey wolf optimizer for bearing intelligent fault diagnosis
In this study, an adaptive kernel dictionary learning method for intelligent fault diagnosis of bearings is proposed. Kernel KSVD (KKSVD) is an excellent dictionary learning method with the capacity to handle nonlinear signals. However, the choice of kernel parameters and sparse level is a key issue, since these parameters respectively determine the form of the high-dimensional kernel space and the capability of KKSVD to learn appropriate atomic information for representing the samples. As a result, it is difficult to achieve the maximum performance of KKSVD by pre-specifying the values of the parameters. To address this issue, an advanced meta-heuristic algorithm – that is, the grey wolf optimizer (GWO) is introduced into the KKSVD. Specifically, an objective function is first designed, in which the parameters to be optimized are involved in the learning process of KKSVD for the bearing train set and then applied to the testing of the bearing validation set to get the classification results. The classification accuracy is fed back to the GWO algorithm which will update the parameters iteratively and output the optimal parameters. Two case studies respectively corresponding to two common situations in bearing fault diagnosis – that is, strong noisy samples and unbalanced samples, are carried out. The analysis results demonstrate the effectiveness of the proposed method for adaptively obtaining the optimal parameters and improving the performance of KKSVD. Furthermore, the proposed method outperforms several state-of-art dictionary methods in terms of diagnosis accuracy and robustness.
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来源期刊
CiteScore
4.50
自引率
19.00%
发文量
81
审稿时长
6-12 weeks
期刊介绍: The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome
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