An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network

Rotating machinery is a very important mechanical device widely used in critical industrial applications. Efficient fault detection and diagnosis are key challenges in the maintenance and operational reliability of rotating machinery. To overcome this problem, a novel fault diagnosis method for rotating machinery based on deep residual neural network (DRNN) and data fusion is proposed. First, the time domain and frequency domain features of the original signal are extracted through the Short-time Fourier transform (STFT) layer, and then the deep residual network and the fusion embedding layer are used to fuse the time domain, frequency domain and spatial domain features to obtain high-quality low-dimensional fusion features. Finally, the fault type is obtained through the classifier. The proposed method is applied to the fault diagnosis of rolling bearing and gearbox, and the performance of the model has been tested comprehensively, including model training test, anti-noise test, fault tolerance test. The results confirm that the proposed method is much more effective and robust for feature learning, model training, anti-noise, fault tolerance and fault diagnosis than other fusion learning methods and single sensor-based methods. This fully reflects the advantages of multi-source information fusion in ensuring the reliable operation of rotating machinery.