Sparsity Enhanced Topological Fractal Decomposition for Smart Machinery Fault Diagnosis
Sparsity Enhanced Topological Fractal Decomposition for Smart Machinery Fault Diagnosis
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用于智能机械故障诊断的稀疏增强拓扑分形分解
DOI：
10.1109/access.2018.2869138
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
Wangpeng He
中科院分区：
文献类型：
--
作者：
Xincheng Cao;Nianyin Zeng;Binqiang Chen;Wangpeng He
基于故障特征自动提取的智能故障诊断方法由于不需要现场人员过多的专业知识而越来越受到人们的青睐。先进的信号处理技术对于确保高效和有效的故障特征分析具有重要意义。多分辨率分析是一种有效的工具，用于解耦多个信号模式中的测量振动信号。然而，目前的多分辨率分析方法仍然不能实现围绕固定分析频率的连续频谱细化。为了解决这个问题，提出了一种新的拓扑分形多分辨率分析（TFMRA）理论。TFMRA采用嵌套集中式小波包聚类的概念，具有同时提取多个故障特征的能力。在数学上，我们证明：1）每个NCWPC是所研究信号的谱域的拓扑子集; 2）所有NCWPC集合在几何上具有共同的自相似分形性质。本文揭示了经典并元多分辨分析与TFMRA之间的一个重要内在联系。根据TFMRA的定义，每个二进小波包都可以唯一地与一个NCWPC相关联，而经典小波包空间被视为所提出的NCWPC的真子集.结合TFMRA的信号分解和机械系统的损伤信息，我们提出了一种改进的稀疏性促进的振动特征分析方法来研究重复性瞬态故障特征。将该方法应用于转子碰摩故障的异常振动特征提取。处理结果表明，纳米成分的瞬态振动，这是由碰摩故障，成功地识别。这些结果进行了比较与其他一些比较技术的基础上稀疏表示。实验结果表明，该方法具有较强的抗噪声能力。
Automatic fault feature extraction-based smart fault diagnosis is becoming more and more popular, as it does not require excessive expertise of on-site staff. Advanced signal processing techniques are of significant importance in order to ensure efficient and effective fault feature analysis. Multi-resolution analysis is an effective tool utilized to decouple multiple signal modes within the measured vibration signal. However, current multi-resolution analyzing methods still cannot enable continuous spectral refinements around fixed analyzing frequencies. To address this problem, a novel theory of topological fractal multi-resolution analysis (TFMRA) is proposed. With the concept of nested centralized wavelet packet cluster (NCWPC), TFMRA is equipped with the ability to extract multiple fault features simultaneously. Mathematically, we prove that: 1) each NCWPC is a topology subset of spectral domain of the investigated signal and 2) all sets of NCWPC share a common self-similar fractal property in geometry. This paper reveals an important intrinsic relation between classical dyadic multi-resolution analysis and TFMRA. That is, each dyadic wavelet packet can be uniquely associated with an NCWPC according to the definitions of TFMRA, and classical wavelet packet spaces are regarded as proper subsets of the proposed NCWPCs. Combining signal decomposition using TFMRA and damage information of a mechanical system, we propose an improved sparsity promoted vibration signature analyzing methodology to investigate repetitive transient fault features. This method was applied to extract abnormal vibration signatures from an experimental rotor test rig with rub-impact faults. Processing results demonstrate that nanocomponents of transient vibrations, which are produced by rub-impact faults, were successfully identified. These results are compared with those of some other comparison techniques based on sparse representation. It is verified that the proposed fault diagnosis method possesses more robust noise resisting capability.
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使用改进的快速空间频谱系综峰度峰图检测瞬态振动特征及其在旋转机械短持续时间数据的机械特征分析中的应用
影响因子：
8.4
作者：
Chen, BinQiang;Zhang, ZhouSuo;Sun, Chuang
通讯作者：
Sun, Chuang
影响因子：
--
作者：
Wangpeng He
通讯作者：
Wangpeng He
影响因子：
8.4
作者：
Caesarendra, Wahyu;Kosasih, Buyung;Zhu, Qiang
通讯作者：
Zhu, Qiang
影响因子：
--
作者：
Guobiao Wang
通讯作者：
Guobiao Wang
改进的集成超小波变换用于基于振动的机械故障诊断
DOI：
10.1115/1.4032568
发表时间：
2016-07
影响因子：
4
作者：
He, Wangpeng;Zi, Yanyang;Wan, Zhiguo;Chen, Binqiang
通讯作者：
Chen, Binqiang