Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks

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Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks

机译：基于一维卷积的长短期内存网络的多传感器轴承故障诊断

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Bearings are the key components of various rotating machinery, and their fault diagnosis is very important for improving production safety and economic efficiency. In this paper, an end-to-end solution with one-dimensional convolutional long short-term memory (LSTM) networks is presented, where both the spatial and temporal features of multisensor measured vibration signals are extracted and then jointed for better bearing fault diagnosis. In addition, the number of time steps in the LSTM layers for the long-term temporal feature extraction is much smaller than the length of the input segments, which can highly reduce the computational complexity of the LSTM layers. The experimental results demonstrate the presented solution has better performance than other methods for bearing fault diagnosis, meanwhile, its adaption to different loads and low signal-to-noise ratios is also verified. (C) 2020 Elsevier Ltd. All rights reserved.

机译：轴承是各种旋转机械的关键部件，它们的故障诊断对于提高生产安全和经济效率非常重要。本文提出了一种具有一维卷积长短期存储器（LSTM）网络的端到端解决方案，其中提取多传感器测量的振动信号的空间和时间特征，然后连接以进行更好的轴承故障诊断。另外，用于长期时间特征提取的LSTM层中的时间步长比输入段的长度小得多，这可以高度降低LSTM层的计算复杂度。实验结果表明，所呈现的解决方案具有比其他轴承故障诊断的其他方法更好的性能，同时还验证了其对不同负载和低信噪比的适应。（c）2020 elestvier有限公司保留所有权利。

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《Measurement》 |2020年第2020期|共8页
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正文语种 eng
中图分类计量学;
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Bearing fault diagnosis; Multiple sensors; One-dimensional convolutional neural network (1D CNN); Long short-term memory (LSTM);

机译：轴承故障诊断;多个传感器;一维卷积神经网络（1D CNN）;长短期内存（LSTM）;

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专利

1. Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks [J] . Measurement . 2020,第期

机译：基于一维卷积的长短期内存网络的多传感器轴承故障诊断
2. A novel percussion-based method for multi-bolt looseness detection using one-dimensional memory augmented convolutional long short-term memory networks [J] . Furui Wang, Gangbing Song Mechanical systems and signal processing . 2021,第Deca期

机译：一种新的基于敲击性的多螺栓松开检测方法，使用一维存储器增强卷积长短短期存储网络
3. A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings [J] . Yiwei Cheng, Kui Hu, Jun Wu, Advanced engineering informatics . 2021,第Apra期

机译：基于卷积神经网络的降解指示器建设和健康预测，用于滚动轴承的双向短期内存网络
4. One-Dimensional Convolutional Neural Networks Based on Exponential Linear Units for Bearing Fault Diagnosis [C] . Hanyang Kong, Qingyu Yang, Zhiqiang Zhang, Chinese Automation Congress . 2018

机译：基于轴承故障诊断指数线性单元的一维卷积神经网络
5. Non-linguistic Vocalization Recognition Based on Convolutional, Long Short-term Memory, Deep Neural Networks [D] . Qiu, Liang. 2018

机译：基于卷积，长短时记忆，深度神经网络的非语言语音识别
6. A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional Self-Normalizing Convolutional Neural Networks [O] . Jingli Yang, Shuangyan Yin, Yongqi Chang, 2020

机译：基于一维自正常化卷积神经网络的旋转机械故障诊断方法
7. A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network [O] . Yangyang Wang, Shuzhan Huang, Juying Dai, 2020

机译：基于SVD和一维卷积神经网络的新型轴承故障诊断方法

Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks

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