Gearbox fault diagnosis based on SDP and MCNN-LSTM

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (15) : 126-132.

WU Shengli1, ZHOU Yi1, XING Wenting2

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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.

Key words

gearbox fault diagnosis / symmetrized dot pattern (SDP) / minimum energy error / multi-scale convolutional neural network (MCNN) / long short term memory (LSTM)

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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

References

[1] Sun H, Wang C, Cao X. An adaptive anti-noise gear fault diagnosis method based on attention residual prototypical network under limited samples[J]. Applied Soft Computing, 2022,125:109120. [2] Luo J, Huang J, Li H. A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis[J]. Journal of Intelligent Manufacturing, 2020,32(2):1-19. [3] Gai J B, Zhong K Y, Du X J, et al. Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm[J]. Measurement, 2021,185:110097. [4] C.A. Pickover. On the use of symmetrized dot patterns for the visual characterization of speech waveforms and other sampled data[J]. The Journal of the Acoustical Society of America, 1986,80(3):955-960. [5] González J, Oro JMF, Delgado L, et al. Symmetrized dot pattern analysis for the unsteady vibration state in a Sirocco fan unit[J]. Applied Acoustics, 2019,152:1-12. [6] Xu X, Liu H, Zhu H, et al. Fan fault diagnosis based on symmetrized dot pattern analysis and image matching[J]. Journal of Sound and Vibration, 2016,374:297-311. [7] 朱霄珣, 罗学智, 叶行飞, 等. 基于深度特征学习的汽轮机转子状态识别方法[J]. 中国电机工程学报, 2021,41(2):432-442. Zhu X X, Luo X Z, Ye X F, et al. State recognition method of turbine rotor based on depth feature learning[J]. Proceedings of the CSEE, 2021,41(2):432-442. [8] Zhu X, Hou D, Zhou P, et al. Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images[J]. Measurement, 2019,138:526-535. [9] 刘昕宇, 姜长泓, 王其铭, 等. 基于SDP图像和MobilenetV2的滚动轴承故障诊断[J]. 组合机床与自动化加工技术, 2023(2):178-182. Liu X Y, Jiang C H, Wang Q M, et al. Fault diagnosis of rolling bearing based on SDP image and mobilenetV2[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2023(2):178-182. [10] Sun Y J, Li S H, Wang Y L, et al. Fault diagnosis of rolling bearing based on empirical mode decomposition and improved manhattan distance in symmetrized dot pattern image[J]. Mechanical Systems and Signal Processing, 2021,159:107817. [11] Pang X, Xue X, Jiang W, et al. An investigation into fault diagnosis of planetary gearboxes using a bispectrum convolutional neural network[J]. IEEE/ASME Transactions on Mechatronics, 2021,26(4):2027-2037. [12] 万安平, 杨洁, 王景霖, 等. 基于深度学习的航空发动机齿轮故障诊断[J], 振动.测试与诊断, 2022,42(6):1062-1067+1239. Wan A P, Yang J, Wang J L, et al. Fault diagnosis of aeroengine gear based on deep learing[J]. Journal of Vibration, Measurement & Diagnosis, 2022,42(6):1062-1067+1239. [13] Zhang Y, Liu W, Wang X, et al. A novel wind turbine fault diagnosis method based on compressed sensing and DTL-CNN[J]. Renewable Energy, 2022,194:249-258. [14] Li X, Li J, Zhao C, et al. Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection[J]. Mechanical Systems and Signal Processing, 2020,142:106740. [15] Abdul ZK, Al-Talabani AK, Ramadan DO. A hybrid temporal feature for gear fault diagnosis using the long short term memory[J]. IEEE Sensors Journal, 2020,20(23):14444-14452. [16] 樊家伟, 郭瑜, 伍星, 等. 基于LSTM神经网络和故障特征增强的行星齿轮箱故障诊断[J]. 振动与冲击, 2021,40(20):271-277. Fan J W, Guo Y, Wu X, et al. Fault diagnosis of planetary gearboxes based on LSTM neural network and fault feature enhancement[J]. Journal of Vibration and Shock, 2021,40(20):271-277. [17] Yuan F, Xiang C, Yu L, et al. Gearbox fault diagnosis based on multi-sensor and multi-channel decision-level fusion based on SDP[J]. Applied Sciences, 2022,12(15):7535. [18] 陈科, 段伟建, 吴胜利, 等. 多深度学习模型决策融合的齿轮箱故障诊断分类方法[J]. 科学技术与工程, 2022,22(12):4804-4811. Chen K, Duan W J, Wu S L, et al. Gearbox fault diagnosis and classification method based on decision fusion of multi-deep learning models[J]. Science Technology and Engineering, 2022,22(12):4804-4811. [19] 吴定海, 任国全, 王怀光, 等. 基于卷积神经网络的机械故障诊断方法综述[J]. 机械强度, 2020,42(5):1024-1032. Wu D H, Ren G Q, Wang H G, et al. The review of mechanical fault diagnosis methods based on convolutional neural network[J]. Journal of Mechanical Strength, 2020,42(5):1024-1032. [20] 魏昱洲, 许西宁. 基于LSTM长短期记忆网络的超短期风速预测[J]. 电子测量与仪器学报, 2019,33(2):64-71. Wei Y Z, Xu X N. Ultra-short-term wind speed prediction model using LSTM networks[J]. Journal of Electronic Measurement and Instrumentation, 2019,33(2):64-71. [21] 王磊, 孙志成, 王磊, 等. 基于DRSN-CW和LSTM的轴承故障诊断[J]. 电子科技大学学报, 2022,51(6):921-927. Wang L, Sun Z C, Wang L, et al. Bearing fault diagnosis based on DRSN-CW and LSTM[J]. Journal of University of Electronic Science and Technology of China, 2022,51(6):921-927. [22] 辛玉忠, 刘常凯. 判断与衡量两个信号相似性的内积表达式[J]. 潍坊学院学报, 2002,2(4):11-12. Xin Y Z, Liu C K. Inherenet product posture about judging two signals resemble quality[J]. Journal of Weifang University, 2002,2(4):11-12. [23] Shao S, McAleer S, Yan R, et al. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning[J]. IEEE Transactions on Industrial Informatics, 2019,15(4):2446-2455. [24] 张前图, 房立清. 基于图像形状特征和LLTSA的故障诊断方法[J]. 振动与冲击, 2016,35(9):172-177. Zhang Q T, Fang L Q. Fault diagnosis method based on image shape features and LLTSA[J]. Journal of Vibration and Shock, 2016,35(9):172-177.