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A fault diagnosis model of rotating machinery based on KD-DenseNet |
WANG Taiyong,GONG Liming,WANG Peng,QIAO Huihui,REN Dong |
School of Mechanical Engineering, Tianjin University, Tianjin 300350, China |
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Abstract The traditional fault diagnosis algorithm lacks good adaptability and generalization due to the complex and changeable working conditions of rotating machinery. To solve the problems, an intelligent fault diagnosis model KD-DenseNet based on kernel dropout (KD) is proposed. Firstly, preprocessing the original vibration signals of various fault states by overlapping and segmenting. Then, the pre-processed data are trained as input of KD-DenseNet, at the same time, dropout is applied to kernel to improve the processing speed and anti-interference ability of the model for vibration signals. Finally, the result of fault type determination is obtained. The application of KD-DenseNet avoids the gradient dispersion phenomenon, improves the extraction efficiency of effective features, and solves the problems that traditional feature extraction methods can not mine features and adapt to task adjustment effectively.
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Received: 17 January 2019
Published: 15 August 2020
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[1] Jing L, Zhao M, Li P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox[J]. Measurement, 2017,111:1-10.
[2] Li Y, Xu M, Wei Y, et al. A new rolling bearing fault diagnosis method based on multiscale permutation entropyand improved support vector machine based binary tree[J]. Measurement, 2016,77:80-94.
[3] Xing Z, Qu J, Chai Y, et al. Bearing Fault Diagnosis Based on Hilbert Marginal Spectrum and Supervised Locally Linear Embedding[J]. Proceedings of 2016 Chinese Intelligent Systems Conference ,2016,405:221-231.
[4] Lee W, Chan G P. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA[J]. Sensors, 2014,14(2):3428-3444.
[5] Moussa H, Dongik L, Emiliano M, et al. Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions[J]. Applied Sciences, 2018,8(8):1392-.
[6] 曲建岭, 余路, 袁涛, 等. 基于一维卷积神经网络的滚动轴承自适应故障诊断算法[J]. 仪器仪表学报, 2018,39(07):134-143.
Qu Jianling, Yu Lu, Yuan Tao, et al. An adaptive fault diagnosis algorithm for rolling bearings based on one-dimensional convolution neural network[J]. Journal of Instruments and Instruments, 2018,39(07):134-143(in Chinese).
[7] Misra M, Yue H H, Qin S J, et al. Multivariate Process Monitoring and Fault Diagnosis by Multi-Scale PCA[J]. Computers & Chemical Engineering, 2002,26(9):1281-1293.
[8] Di T, Lin F, Zhang Y. HSIC-based Kernel independent component analysis for fault monitoring[J]. Chemometrics & Intelligent Laboratory Systems, 2018,178:47-55.
[9] Kumar A, Kumar R. Time-frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump[J]. Measurement, 2017,108:119-133.
[10] Ollis D. Kinetics of Photocatalytic, Self-Cleaning Surfaces: A Decision Tree Approach for Determination of Reaction Order[J]. Applied Catalysis B: Environ-mental, 2019,242:431-440.
[11] Feng D, Cheng X, Guo J, et al. Research on UPFC fault diagnosis based on wavelet transform and support vector machines[C]// IEEE International Conference on Electronic Measurement & Instruments. 2018.
[12] Chen J, Liu Z, Wang H, et al. Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network[J]. IEEE Transactions on Instrumentation & Measurement, 2018,PP(99):1-13.
[13] Qiao H, Wang T, Wang P, et al. A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series[J]. Sensors, 2018,18(9):2932-.
[14] 李恒, 张氢, 秦仙蓉, 等. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击, 2018,37(19):124-131.
Li Heng, Zhang Qing, Qin Xianrong, et al. Bearing Fault Diagnosis Method Based on Short-time Fourier Transform and Convolutional Neural Network[J]. Vibration and shock,2018,37(19):124-131(in Chinese).
[15] HUANG G, LIU Z, MAATEN L V D, et al. Densely Connected Convolutional Networks[C]. IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2017:2261-2269.
[16] Jia F, Lei Y, Guo L, et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines[J]. Neurocomputing, 2018,272:619-628
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