并行稀疏滤波在轴承声信号下的故障诊断
收稿日期: 2021-12-31
修回日期: 2022-01-14
录用日期: 2022-02-22
网络出版日期: 2022-03-30
基金资助
国家自然科学基金(52005303);山东省自然科学基金(ZR2020QE157)
Parallel sparse filtering for fault diagnosis under bearing acoustic signal
Received date: 2021-12-31
Revised date: 2022-01-14
Accepted date: 2022-02-22
Online published: 2022-03-30
Supported by
National Natural Science Foundation of China(52005303);Natural Science Foundation of Shandong Province(ZR2020QE157)
振动信号是航空发动机故障监测的常用信号。由于航空发动机结构复杂，对振动传感器的布置要求日益严格。声学信号以其非接触式、易布置、低成本的优点，在轴承智能故障诊断中引起了广泛的关注。然而，由于航空发动机内声信号所处的环境噪声较强，传统的轴承故障诊断方法无法实现精确的特征提取。为此，研究有效的特征提取方法实现轴承声信号下的智能故障诊断显得尤为重要。稀疏表示是智能故障诊断中的一个研究热点，在稀疏特征提取方面显示出强大的力量。对强噪声下的声信号进行有效的稀疏特征提取，可为轴承的非接触式故障诊断提供解决路径。提出一种基于并行稀疏滤波的轴承故障诊断方法，能够实现对轴承声信号的稀疏特征提取。并行稀疏滤波通过在传统稀疏滤波的基础上增加另一个归一化方向来实现进一步的稀疏特征提取，然后采用权值归一化方法约束训练得到的权值矩阵。最后，通过仿真和实验数据验证了所提方法的优越性。结果表明，并行稀疏滤波能够实现轴承声信号的有效稀疏特征提取和精准分类，可用于声学信号下的轴承智能故障诊断。
王金瑞 , 季珊珊 , 张宗振 , 初振云 , 韩宝坤 , 鲍怀谦 . 并行稀疏滤波在轴承声信号下的故障诊断[J]. 航空学报, 2023 , 44(4) : 426887 -426887 . DOI: 10.7527/S1000-6893.2022.26887
Vibration signal is commonly used in fault monitoring of aeroengine. However， the arrangement of vibration sensors is increasingly strict due to the complex structure of aeroengine. Acoustic signal has attracted extensive attention in intelligent fault diagnosis of bearings because of its advantages of non-contact， easy arrangement and low cost. However， traditional bearing fault diagnosis methods cannot achieve accurate feature extraction due to the strong ambient noise in the aeroengine acoustic signal. Therefore， to realize intelligent fault diagnosis under bearing acoustic signals， it is particularly important to study effective feature extraction methods. Sparse representation is a research hotspot in intelligent fault diagnosis， which shows great power in sparse feature extraction. Effective sparse feature extraction of acoustic signals under strong noise can provide a solution for non-contact fault diagnosis of bearings. In this paper， a novel fault diagnosis method based on parallel sparse filtering is proposed to realize sparse feature extraction of bearing acoustic signals. Specially， parallel sparse filtering achieves further sparse feature exaction by adding another normalization direction on the basis of traditional sparse filtering， and then the weight normalization method is used to constrain the weight matrix obtained by network training. Finally， the superiority of the proposed method is validated by simulation and experimental dataset. The results show that parallel sparse filtering can realize effective feature extraction and accurate classification of bearing acoustic signals， and can be used for mechanical intelligent fault diagnosis under acoustic signals.
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