国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 信号处理
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| 标 题： |
A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis
一种基于数据的增强滚动轴承故障分析振动信号分解方法 |
| 作 者： |
Grasso, M.
Chatterton, S.
Pennacchi, P.
Colosimo, B. M. |
| 关键词： |
Empirical Mode Decomposition [经验模态分解]
Combined Mode Functions [ 组合模式功能]
Vibration [ 振动]
Bearing [ 轴承]
Fault detection [ 故障检测] |
| 出版年： |
2016 |
| 期刊名： |
Mechanical Systems & Signal Processing
机械系统与信号处理 |
| 年卷期： |
2016, vol.81 |
| 摘 要： |
Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits. (C) 2016 Elsevier Ltd. All rights reserved.
旋转机械的健康状态分析与诊断需要能够准确表征传感器信号的信息内容，以检测和识别潜在的故障特征。时频分析在此过程中发挥着基础性作用，因为它既能判断故障的存在，又能确定其成因。不同时间-频率尺度上属于正常或异常状态的信号成分分离，是一个重要挑战，促使人们开发出有效的多尺度信号分解方法。在此框架下，经验模态分解（EMD）因其数据驱动和自适应的特性而成为一种灵活的工具。然而，EMD通常会将原始信号过度分解为大量固有模态函数（IMFs）。如何选择最具相关性的IMF是困难的问题，而现有文献中缺乏自动化的有效方法，能够通过避免产生虚假或无意义的模态，实现对少数物理意义明确的模态进行合成分解。本文提出了一种新颖的自动化方法，旨在将信号分解为尽可能少的相关模态，称为组合模态函数（CMFs），每个CMF由具有相似特性的相邻IMF之和构成。最终CMF的数量通过完全数据驱动的方式确定，从而在不丢失任何信息的前提下，对信号内容进行更精确的表征。文中还提出了一种基于频率谱概率密度函数的新准则，用于评估相邻CMF之间的差异性。该方法适用于分析旋转机械在运行寿命内可能周期性采集的振动信号。此外，基于实验数据展示了滚动轴承故障分析中该方法的性能及其带来的优势。（C）2016 Elsevier Ltd. 保留所有权利。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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