基于SDP和MCNN-LSTM的齿轮箱故障诊断方法

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (15) : 126-132.

论文

吴胜利1，周燚1，邢文婷2

作者信息 +

Gearbox fault diagnosis based on SDP and MCNN-LSTM

WU Shengli1, ZHOU Yi1, XING Wenting2

Author information +

文章历史 +

摘要

齿轮箱在长期使用过程中，不可避免的会产生齿轮故障和轴承故障，严重影响传动精度和设备运行安全。基于此，针对齿轮箱常见故障类型，研究多通道对称点图案（symmetrized dot pattern， SDP）数据处理方法，并利用最小能量误差法实现SDP关键参数的选取。结合多尺度卷积神经网络（multi-scale convolutional neural network, MCNN）的空间处理优势、长短时记忆网络（long short term memory, LSTM）的时间处理优势及其良好的抗噪性和鲁棒性，提出了一种基于SDP和MCNN-LSTM的齿轮箱故障诊断模型。同时利用东南大学齿轮箱数据集，验证了基于SDP和MCNN-LSTM的齿轮箱故障诊断方法对齿轮和轴承常见故障类型特征提取的有效性，并与现有其他故障诊断方法进行对比，结果表明了所提方法具有更高的精度。

Abstract

In the long-term use of the gearbox, gear failure and bearing failure will inevitably occur, which will seriously affect the transmission accuracy and equipment operation safety. Aiming at the common fault types of gearboxes, the multi-channel symmetrized dot pattern (SDP) data processing method was studied. The minimum energy error method was used to realize the selection of key SDP parameters. Combining the spatial processing advantages of multi-scale convolutional neural network (MCNN), the time processing advantages of long short term memory (LSTM), and its excellent noise immunity and robustness, a gearbox fault diagnosis model based on SDP and MCNN-LSTM was proposed. The effectiveness of the gearbox fault diagnosis method based on SDP and MCNN-LSTM in the extraction of common fault types of gears and bearings is verified using the gearbox dataset of Southeast University. Compared with other existing fault diagnosis methods, the results showed that the proposed method has higher accuracy.

导出引用

吴胜利1，周燚1，邢文婷2. 基于SDP和MCNN-LSTM的齿轮箱故障诊断方法[J]. 振动与冲击, 2024, 43(15): 126-132

WU Shengli1, ZHOU Yi1, XING Wenting2. Gearbox fault diagnosis based on SDP and MCNN-LSTM[J]. Journal of Vibration and Shock, 2024, 43(15): 126-132

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