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ZhangWei1993
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CNN for mechanical fault diagnosis

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Mechanical-Fault-Diagnosis-Based-on-Deep-Learning

CNN for mechanical fault diagnosis

These codes serve for two papers: 'Rolling Element Bearings Fault Intelligent Diagnosis Based on Convolutional Neural Networks Using Raw Sensing Signal'(paper1) and 'Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input'(paper2).

Prerequisite, Matlab 2013a, Python 2.7.11, Tensorflow (better in ubuntu14.04).  

To start with, you should run image_matrix.m to prepare your own data.

second, disorderimages.py and inputbear_data.py should be used to tansform your data into the input format of tensorflow.

last, you may choose to run mnistb.py or mnistc.py for paper1 or mnist2d.py for paper_2.

如果大家对以上研究感兴趣,可以进一步参考我的这两篇论文:A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals与A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load,更详细的请看我的硕士毕业论文:基于卷积神经网络的轴承故障诊断算法研究。若使用,请引用,谢谢。

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