Rolling element bearing fault diagnosis using convolutional neural network and vibration image
By Duy-Tang Hoang, Hee-Jun Kang. Cognitive Systems Research, 2019
420 citations recorded by the source corpus.
Abstract Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.
Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/5b571f831aad8f872d25dee520fc381ba2379790
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In Control and Systems Engineering
In Mechanical Engineering
In Mechanics of Materials
Citations in the corpus
- A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “[54] converted raw vibration signals into vibration images, then a simple two-layer convolutional model was constructed for rolling bearing fault classification.”