Dictionary Learning Method for Cyclostationarity Maximization and Its Application to Bearing Fault Feature Extraction

IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-01 DOI:10.1109/TIM.2024.3484531
Weihao Zhang;Cai Yi;Lei Yan;Qi Liu;Qiuyang Zhou;Pengfei He;Le Ran;Yunzhi Lin
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Abstract

It has been demonstrated that fast convolutional sparse dictionary learning (FCSDL) is a useful instrument for diagnosing rolling bearing faults and can recover rolling bearing fault shocks unaffected by random slippage. However, although FCSDL is not impacted by random fluctuations and can rapidly reconstruct fault shock without truncating the signal, its performance for repetitive fault shock reconstruction is not optimal when dealing with strong noise vibration signals. Therefore, this article proposes cyclostationary convolutional sparse dictionary learning (CCSDL), which is guided by fault features (cyclostationarity) to achieve the greatest signal reconstruction performance. First, the proposed method is based on the rotation frequency, and various frequency-band-covering components in the vibration signal are reconstructed successively. In the meanwhile, the harmonic significance index (HSI), which can indicate the cyclostationarity of the fault shock, evaluates the fault characteristics of each reconstruction result and finally obtains the most significant reconstruction result. Compared with FCSDL and variational mode decomposition (VMD), the proposed method performs far superior in signal reconstruction when processing low SNR vibration data.
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循环最大化的字典学习法及其在轴承故障特征提取中的应用
研究表明,快速卷积稀疏字典学习(FCSDL)是诊断滚动轴承故障的有效工具,可以恢复不受随机滑动影响的滚动轴承故障冲击。然而,尽管快速卷积稀疏字典学习不受随机波动的影响,并能在不截断信号的情况下快速重建故障冲击,但在处理强噪声振动信号时,其重复性故障冲击重建性能并不理想。因此,本文提出了循环静止卷积稀疏字典学习(CCSDL),该方法以故障特征(循环静止)为导向,以实现最佳的信号重构性能。首先,该方法基于旋转频率,依次重建振动信号中的各种频带覆盖成分。同时,通过谐波重要度指数(HSI)来评估每个重构结果的故障特征,从而获得最重要的重构结果,谐波重要度指数可以表示故障冲击的周期性。在处理低信噪比振动数据时,与 FCSDL 和变异模态分解(VMD)相比,所提出的方法在信号重构方面表现更为出色。
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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