Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

TLDR

Early detection of motor faults is critical, yet conventional neural network systems rely on hand‑crafted feature extraction and classification blocks that are computationally expensive and hinder real‑time performance. The authors propose a fast, accurate motor fault‑detection system that fuses feature extraction and classification into a single 1‑D CNN. The method applies a 1‑D CNN directly to raw motor signals, eliminating separate feature extraction and reducing computational and hardware demands. Experiments on real motor data confirm the method’s effectiveness for real‑time condition monitoring.

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.