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CNN for mechanical fault diagnosis

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

last, you may choose to run or for paper1 or 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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