电工技术学报  2022, Vol. 37 Issue (16): 4084-4093    DOI: 10.19595/j.cnki.1000-6753.tces.210306
电机及其系统 |
基于改进正余弦算法优化堆叠降噪自动编码器的电机轴承故障诊断
李兵, 梁舒奇, 单万宁, 曾文波, 何怡刚
可再生能源接入电网技术国家地方联合工程实验室(合肥工业大学) 合肥 230009
Motor Bearing Fault Diagnosis Based on Improved Sine and Cosine Algorithm for Stacked Denoising Autoencoders
Li Bing, Liang Shuqi, Shan Wanning, Zeng Wenbo, He Yigang
National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei University of Technology Hefei 230009 China
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摘要 轴承是电机的重要组成部分,其故障振动信号存在噪声干扰,导致特征提取困难,堆叠降噪自动编码器(SDAE)通过将输入数据随机置零训练网络可以有效抑制噪声干扰。此外,不理想的超参数组合易引起SDAE诊断性能不佳。因此,提出一种基于改进正余弦算法(ISCA)优化SDAE的电机轴承故障诊断方法。首先,在改进正余弦算法(SCA)粒子值更新公式中引入非线性惯性权重并对控制参数加入余弦变化构造ISCA,利用ISCA对SDAE超参数自适应选取;其次,利用具有最优网络结构的SDAE模型的无监督自学习特征提取方法提取振动信号特征参数,从而实现更好的故障诊断效果。仿真及现场实验结果表明,该方法收敛速度快、诊断准确率高,而且具有较强的鲁棒性,在电机轴承故障诊断方面具有较好的应用前景。
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李兵
梁舒奇
单万宁
曾文波
何怡刚
关键词 堆叠降噪自动编码器改进正余弦算法电机轴承故障诊断自适应    
Abstract:The bearing is an important part of motor, but its fault vibration signal has noise interference, which makes feature extraction difficult. Stacked denoising auto encoders (SDAE) can effectively suppress the noise interference by setting the input data to zero and training the network randomly. In addition, the unsatisfactory combination of hyperparameters is likely to cause poor diagnostic performance of SDAE. Therefore, an improved sine cosine algorithm (ISCA) was proposed to optimize SDAE for motor bearing fault diagnosis. Firstly, the nonlinear inertia weight was introduced into the particle value update formula of sine cosine algorithm (SCA), and the control parameters were added with cosine change to construct ISCA. The hyperparameters of SDAE were adaptively selected by ISCA. Secondly, the unsupervised self-learning feature extraction method of SDAE model with optimal network structure was used to extract the characteristic parameters of vibration signals, so as to achieve better fault diagnosis effect. Simulation and field experiment results show that the proposed method has high convergence speed, high diagnosis accuracy and strong robustness, and has a good application prospect in motor bearing fault diagnosis.
Key wordsStacked denoising auto encoders    improved sine cosine algorithm    motor bearing    fault diagnosis    adaptive   
收稿日期: 2021-03-11     
PACS: TM307  
基金资助:国家自然科学基金项目(51777050)和国家自然科学基金重点项目(51637004)资助
通讯作者: 李兵,男,1973年生,教授,博士生导师,主要研究方向为故障诊断、智能电网技术。E-mail: libinghnu@163.com   
作者简介: 梁舒奇,女,1997年生,硕士研究生,主要研究方向为电机故障诊断。E-mail: 1225463169@qq.com
引用本文:   
李兵, 梁舒奇, 单万宁, 曾文波, 何怡刚. 基于改进正余弦算法优化堆叠降噪自动编码器的电机轴承故障诊断[J]. 电工技术学报, 2022, 37(16): 4084-4093. Li Bing, Liang Shuqi, Shan Wanning, Zeng Wenbo, He Yigang. Motor Bearing Fault Diagnosis Based on Improved Sine and Cosine Algorithm for Stacked Denoising Autoencoders. Transactions of China Electrotechnical Society, 2022, 37(16): 4084-4093.
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