A Novel Deep Learning Network via Multiscale Inner Product With Locally Connected Feature Extraction for Intelligent Fault Detection

Intelligent fault detection is an important application of artificial intelligence and has been widely used in many mechanical systems. The shipborne antenna that is a typical and an important mechanical system plays an irreplaceable role in ships. Considering the tough working environment and heavy background noise, fault detection is difficult for the shipborne antenna. Therefore, the paper presents an intelligent fault detection method via multiscale inner product with locally connected feature extraction for shipborne antenna fault detection. Inspired by inner product principle, this paper takes advantage of inner product to capture fault information in the vibration signals and detect the faults in rolling bearing of the shipborne antenna. Meanwhile, multiscale analysis is employed in two layers of the network to improve the feature extraction ability. The local features under different scales are collected and used for fault classification. Finally, the proposed method is verified by three datasets and comparison methods are also developed to show its superiority. Results show that the proposed method can learn sensitive features directly from raw vibration signals and detect the faults in rolling bearing of shipborne antenna effectively.