| 引用本文: | 张氢,江伟哲,李恒.MCKD-Teager能量算子结合LSTM的滚动轴承故障诊断[J].哈尔滨工业大学学报,2021,53(7):68.DOI:10.11918/202006114 |
| ZHANG Qing,JIANG Weizhe,LI Heng.Combined MCKD-Teager energy operator with LSTM for rolling bearing fault diagnosis[J].Journal of Harbin Institute of Technology,2021,53(7):68.DOI:10.11918/202006114 |
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| 摘要: |
| 为解决滚动轴承故障时产生的信号具有强背景噪声而导致弱周期冲击特征难提取,以及在对轴承故障模式进行智能诊断时一般的诊断模型对故障振动信号的时序特征识别效果不强这两大问题,提出一种基于最大相关峭度解卷积（MCKD）、Teager能量算子和长短期记忆网络（LSTM）的故障诊断方法。 使用MCKD算法对滚动轴承振动信号进行降噪处理,提取出信号中被噪声掩盖的周期冲击特征,并利用Teager能量算子检测信号的瞬态冲击,得到Teager能量序列；将结果分为训练集和测试集,将训练集输入到建立的LSTM故障诊断模型中进行学习,不断更新网络参数并提取出时间维度的特征信息；将训练好参数的LSTM模型应用于测试集,输出故障诊断结果。 实验结果表明,提出的方法以端到端模式可以一次性诊断多种类型、尺寸的故障,具有很高的识别精度,是一种可以有效利用强背景噪声信号中时序特征的故障诊断方法。 |
| 关键词: 滚动轴承 故障诊断 最大相关峭度解卷积 Teager能量算子 长短期记忆网络 |
| DOI：10.11918/202006114 |
| 分类号:TH212,TH213.3 |
| 文献标识码:A |
| 基金项目:上海市科学技术委员会科研计划项目(17DZ1204602) |
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| Combined MCKD-Teager energy operator with LSTM for rolling bearing fault diagnosis |
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ZHANG Qing,JIANG Weizhe,LI Heng
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(School of Mechanical Engineering, Tongji University, Shanghai 201804, China)
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| Abstract: |
| To solve these two problems that it is difficult to extract the characteristics of weak periodic impact caused by the strong background noise of the signal generated during the rolling bearing fault, and the general diagnosis model does not have a strong recognition effect on the timing characteristics of the fault vibration signal during the intelligent diagnosis of the bearing fault mode, this paper put forward a fault diagnosis method based on the maximum correlation kurtosis deconvolution(MCKD)algorithm, Teager energy operator and long short-term memory(LSTM). Firstly, the rolling bearing vibration signal is denoised by MCKD algorithm, the periodic impact characteristics of the signal which are covered by noise are extracted, the Teager energy operator is used to detect the transient impact of the signal and the Teager energy sequence is obtained. The results are then divided into training sets and test sets, the training set is input into the established LSTM fault diagnosis model for learning. Finally, the LSTM model with appropriate parameters is applied to the test set to output fault diagnosis results. The experimental results show that the proposed method can diagnose faults of various types and sizes at one time and has high identification accuracy. It is a fault diagnosis method that can effectively utilize the timing characteristics of strong background noise signals. |
| Key words: rolling bearing fault diagnosis maximum correlation kurtosis deconvolution Teager energy operator long short-term memory |
