IEEE Transactions on Industrial Electronics· 2016Q1
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
- 1,376citations
- Q1SCImago
- 2016year
Short summary
A 1D convolutional neural network (CNN) directly processes raw motor signals for real-time fault detection, fusing feature extraction and classification into a single learning process.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a 1D CNN for motor fault detection that directly uses raw signal data.
- Integrates feature extraction and classification into a single learning model.
- Eliminates the need for separate, hand-crafted feature extraction algorithms.
- Demonstrates effectiveness for real-time motor condition monitoring via experimental results.
AI-generated from the title and abstract; the full text is not read.
Abstract
Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.
The authors' abstract, as published at the source. IEEE Transactions on Industrial Electronics, 2016 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Control and Systems Engineering
Control and Systems EngineeringEngineering