1. Fault State Recognition of Rolling Bearing Based Fully Convolutional Network
- Author
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Dongli Song, Fan Zhang, Yongquan Jiang, Wei Chen, and Wendong Zhang
- Subjects
0209 industrial biotechnology ,Bearing (mechanical) ,Correctness ,General Computer Science ,business.industry ,Computer science ,Feature extraction ,General Engineering ,Cognitive neuroscience of visual object recognition ,Pattern recognition ,02 engineering and technology ,Fault (power engineering) ,Convolutional neural network ,law.invention ,Time–frequency analysis ,020901 industrial engineering & automation ,law ,0202 electrical engineering, electronic engineering, information engineering ,Spectrogram ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
To solve the problem of determining the fault damage of rolling bearings, a fault diagnosis method for intelligent classification of vibration signals with different fault locations and different damage degrees is proposed. First, the research object is the laboratory dataset. By transforming into spectrograms, this can preserve the original information of the time-domain signal to a greater extent. Then, we use a deep, fully convolutional neural network to train the dataset. It has a rapid convergence and the accuracy is up to 100%. Second, in order to verify the correctness of the model, we take the service data on the real line as the research object, and the accuracy rate is as high as 99.22%. Compared with some other machine learning algorithms, our method boasts better generalization capability and accuracy and could be applied to practical engineering.
- Published
- 2019
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