摘要
工程装备轴承故障工况特征常被外在信息淹没,为了对故障数据有效提取,提出了粒子群寻优与稀疏重构相结合的降噪滤波方法,选取Laplace小波基进行参数寻优与字典预构,进而对轴承的振动信号进行稀疏重构。通过对实验数据施加2 dB的高斯白噪声模拟工程环境下的轴承信号,将优化的稀疏重构算法与巴特沃斯滤波器、小波阈值去噪算法进行对比。结果显示:在峰值信噪比与波形相似性等参数上,所提方法的效果更优,所得的重构信号内外圈故障特征频率与理论特征频率相接近,在充分过滤噪声后,可保留原始特征信息,为后期的故障诊断提供良好的数据基础。
The characteristics of engineering equipment bearing fault conditions are often overwhelmed by the external information.In order to effectively extract the fault data,a noise reduction filtering method combining the particle swarm optimization with the sparse reconstruction is proposed.Laplace wavelet base is selected for parameter optimization and dictionary prediction structure,and then the vibration signal of the bearing is sparsely reconstructed.By applying 2 dB Gaussian white noise to the experimental data to simulate the bearing signal in the engineering environment,the optimal sparse reconstruction algorithm is compared with the Butterworth filter and wavelet threshold denoising algorithm.The results show that the present method is more effective in terms of parameters such as peak signal-to-noise ratio and waveform similarity.The fault characteristic frequencies of the inner and outer rings of the reconstructed signal are close to the theoretical characteristic frequencies.After the noise is fully filtered,the original characteristic information provides a good data basis for the later fault diagnosis.
作者
张泽宇
石泽
惠记庄
任余
张旭辉
ZHANG Zeyu;SHI Ze;HUI Jizhuang;REN Yu;ZHANG Xuhui(Key Laboratory of Road Construction Technology and Equipment,Ministry of Education,Chang′an University,Xi′an 710064,China;Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring,Xi′an University of Science and Technology,Xi′an 710054,China;Research Center,Tibet Tianlu Co.,Ltd.,Lhasa 850000,China)
出处
《机械科学与技术》
CSCD
北大核心
2021年第9期1361-1369,共9页
Mechanical Science and Technology for Aerospace Engineering
基金
陕西省自然科学基金项目(2019JZ-10)
中国博士后科学基金项目(2019M663913XB)
中央高校基本科研业务费资助项目(300102250106,300102251201)
陕西省矿山机电装备智能监测重点实验室开放基金项目(SKL-MEEIM201907)
中国博士后国际交流计划项目(2020056)
西藏自治区科技计划项目(XZ2019TL-G-02)。
关键词
Laplace小波
稀疏重构
粒子群算法
工程装备轴承
Laplace wavelet
sparse reconstruction
particle swarm algorithm
engineering equipment bear
