基于等變化自適應(yīng)源分離算法的滾動(dòng)軸承故障信號(hào)自適應(yīng)盲提取

孫瑾鈴; 張偉濤; 樓順天

doi:10.11999/JEJT190722

基于等變化自適應(yīng)源分離算法的滾動(dòng)軸承故障信號(hào)自適應(yīng)盲提取

doi: 10.11999/JEJT190722

西安電子科技大學(xué)電子工程學(xué)院西安 710071

基金項(xiàng)目: 國家自然科學(xué)基金(61571339)，陜西省創(chuàng)新人才推進(jìn)計(jì)劃-青年科技新星項(xiàng)目(2018KJXX-019)

詳細(xì)信息

作者簡介:
孫瑾鈴：女，1995年生，博士生，研究方向?yàn)槊ば盘?hào)處理

張偉濤：男，1983年生，副教授，碩士生導(dǎo)師，研究方向?yàn)槊ば盘?hào)處理

樓順天：男，1962年生，教授，博士生導(dǎo)師，研究方向?yàn)樯窠?jīng)網(wǎng)絡(luò)信息處理與應(yīng)用、模糊信息處理與應(yīng)用、盲信號(hào)處理、現(xiàn)代信號(hào)智能處理、智能控制技術(shù)

通訊作者:
張偉濤　zhwt-work@foxmail.com

中圖分類號(hào): TN911.7
計(jì)量
- 文章訪問數(shù): 2306
- HTML全文瀏覽量: 448
- PDF下載量: 51
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-09-17
- 修回日期: 2020-04-29
- 網(wǎng)絡(luò)出版日期: 2020-05-13
- 刊出日期: 2020-10-13

Adaptive Blind Extraction of Rolling Bearing Fault Signal Based on Equivariant Adaptive Separation via Independence

School of Electronic Engineering, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61571339), The Innovative Talents Promotion Program of Shaanxi Province (2018KJXX-019)

摘要

摘要: 針對(duì)復(fù)雜工況下滾動(dòng)軸承故障信號(hào)盲提取問題，該文提出一種獨(dú)立分量分析(ICA)中非線性函數(shù)自適應(yīng)選擇方法，解決了等變化自適應(yīng)源分離算法(EASI)在多類振動(dòng)源共存的情況下無法分離軸承故障信號(hào)的問題。此外，為了解決在線盲分離算法穩(wěn)態(tài)誤差與收斂速率的平衡問題，提出基于模糊邏輯的自適應(yīng)迭代步長選擇方法，極大地提高了學(xué)習(xí)算法的收斂速度，且穩(wěn)態(tài)誤差更小。軸承故障數(shù)據(jù)的盲提取仿真結(jié)果驗(yàn)證了算法的性能。
- 盲信號(hào)分離 /
- 故障檢測 /
- 超高斯 /
- 亞高斯 /
- 模糊邏輯
Abstract: For the problem of blind extraction of rolling bearing fault signals under complex working conditions, an adaptive selection method of non-linear functions in Independent Component Analysis (ICA) is proposed, which solves the problem that Equivariant Adaptive Separation via Independence(EASI) can not separate bearing fault signals when multiple vibration sources coexist. In addition, in order to balance the steady-state error and convergence rate of the online blind separation algorithm, an adaptive iterative step selection method based on fuzzy logic is proposed, which improves greatly the convergence speed of the learning algorithm and reduces the steady-state error. The simulation results of blind extraction of bearing fault data verify the performance of the proposed algorithm.
- Blind signal separation /
- Fault detection /
- Super-Gaussian /
- Sub-Gaussian /
- Fuzzy logic

HTML全文

圖 1 模糊系統(tǒng)的輸入與輸出

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圖 2 源信號(hào)波形及其幅值分布

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圖 3 觀測信號(hào)及分離信號(hào)包絡(luò)譜

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圖 4 算法的性能比較

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表 1 模糊推理規(guī)則

	$\mu {{ = S1} }$	$\mu {{ = S2} }$	$\mu {{ = M} }$	$\mu {{ = B} }$
${D_i}{{ = S1} }$	${{S1} }$	${{S1} }$	${{S2} }$	${{M2} }$
${D_i}{{ = S2} }$	${{S1} }$	${{S2} }$	${{M1} }$	${{M2} }$
${D_i}{{ = M} }$	${{M1} }$	${{M1} }$	${{M2} }$	${{B1} }$
${D_i}{{ = B} }$	${{M2} }$	${{M2} }$	${{B1} }$	${{B2} }$

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表 2 算法的成功率比較

算法名稱	成功率(%)
EASI, $g(x) = {x^3}$	0
EASI, $g(x) = \tanh (x)$	12
本文算法，使用固定步長	88
本文算法，使用模糊邏輯步長	97

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表 3 算法的性能比較

算法	ISR
SOBI	0.069
FastICA, $g( \cdot ) = \tanh ( \cdot )$	0.140
FastICA, $g( \cdot ) = {( \cdot )^3}$	0.170
FastICA, $g( \cdot ) = ( \cdot )\exp ( - {( \cdot )^2}/2)$	0.160
本文算法，使用模糊邏輯步長	0.110

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