Abstract
The diagnosis of failures in high-speed machining centers and other rotary machines is critical in manufacturing systems, because early detection can save a representative amount of time and cost. Fault diagnosis systems generally have two blocks: feature extraction and classification. Feature extraction affects the performance of the prediction model, and essential information is extracted by identifying high-level abstract and representative characteristics. Deep learning (DL) provides an effective way to extract the characteristics of raw data without prior knowledge, compared with traditional machine learning (ML) methods. A feature learning approach was applied using one-dimensional (1-D) convolutional neural networks (CNN) that works directly with raw vibration signals. The network structure consists of small convolutional kernels to perform a nonlinear mapping and extract features; the classifier is a softmax layer. The method has achieved satisfactory performance in terms of prediction accuracy that reaches ∼99 % and ∼97 % using a standard bearings database: the processing time is suitable for real-time applications with ∼8 ms per signal, and the repeatability has a low standard deviation <2 % and achieves an acceptable network generalization capability.
Original language | English |
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Pages (from-to) | 27-40 |
Number of pages | 14 |
Journal | Smart and Sustainable Manufacturing Systems |
Volume | 3 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Feb 2019 |
Bibliographical note
Funding Information:The authors thank Tecnológico de Monterrey and Consejo Nacional de Ciencia y Tecnología for their support.
Publisher Copyright:
© 2019 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Control and Systems Engineering
- Industrial and Manufacturing Engineering