Real-Time Motor Fault Detection by 1D Convolutional Neural Networks
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||IEEE Transactions on Industrial Electronics|
|Publication status||Published - 2016|
|Publication type||A1 Journal article-refereed|
Early detection of the motor faults is essential and Artificial Neural Networks (ANNs) 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 sub-optimal choice and require a significant computational cost that will prevent their usage for realtime applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault detection system using 1D Convolutional Neural Networks (CNNs) 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.
- Deep learning, Machine learning, Motor fault detection