首页|融合OCEEMDAN的多模态互量纲一化与宽度学习改进的智能故障诊断

融合OCEEMDAN的多模态互量纲一化与宽度学习改进的智能故障诊断

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滚动轴承作为旋转机械的重要组成部分,在恶劣环境运行导致振动信号具有非线性和非平稳的特点,使得区分故障信号和正常信号变得困难.针对此,提出一种结合多模态互量纲一化(MMDI)与宽度学习系统(BLS)的智能故障诊断方法.通过优化完全自适应噪声集合经验模态(OCEEMDAN)与小波阈值对轴承观测信号进行分解处理,对有效的本征模态函数(IMF)重构并提取MDI,构建了一批MMDI;采用反向传播算法(BP)与堆叠模块方式优化BLS,改进的BLS算法能够快速识别不同的故障类型;最后通过凯斯西储大学轴承数据中心与某实验室提供的轴承数据集对所提方法进行验证,平均准确率分别为99.8％与100％,验证了方法的有效性.
Fusion of OCEEMDAN for MMDI with BLS Improved for Fault Diagnosis
Rolling bearings as an important part of rotating machinery,it operates in harsh environments resulting in non-linear and non-smooth vibration signals,which makes it difficult to distinguish between fault signals and normal signals.In view of this,intelligent fault diagnosis method combining multi-modal mutual dimensionless indicators(MMDI)and broad learning system(BLS)was pro-posed.The optimization complete ensemble empirical mode decomposition with adaptive noise(OCEEMDAN)was used to decompose and preprocess the observable signal of the bearing with wavelet threshold,and the effective intrinsic mode function(IMF)was recon-structed and MDI was extracted to construct the MMDI.The back propagation algorithm and superposition module mode were used to op-timize the BLS,which could quickly identify different types of faults.Finally,the proposed method was verified through the datasets pro-vided by Case Western Reserve University Bearing Data Center and some laboratory,the average accuracy was 99.8％and 100％,re-spectively,the portability and effectiveness of the method were verified.

complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)feature extractionmutual dimen-sionless indicatorsbroad learning systemfault diagnosis

李春林、陈滢、胡钦太、柳琼青、熊建斌、张清华

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广东工业大学计算机学院,广东广州 510006

广东茂名农林科技职业学院智能工程系,广东茂名 525000

揭阳职业技术学院外语系,广东揭阳 522000

广州市智慧建筑设备信息集成与控制重点实验室,广东广州 510665

广东石油化工学院,广东茂名 525000

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完全自适应噪声集合经验模态分解(CEEMDAN) 特征提取 互量纲一化指标 宽度学习系统(BLS) 故障诊断

国家自然科学基金重点项目国家自然科学基金面上项目广东省联合基金重点项目广东省自然科学基金面上项目广东茂名农林科技职业学院科研重点项目

6223700162073090U22A202212023A15150114232022GMNKY01

2024

10.3969/j.issn.1001-3881.2024.08.029

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子：0.32
ISSN：1001-3881
年,卷(期)：2024.52(8)