A novel fusion diagnosis method for rotor system fault based on deep learning and multi-sourced heterogeneous monitoring data

Deep learning-based fault diagnosis has been acclaimed for its superiority in adaptively mining salient features. The monitoring data used as the input of deep learning typically includes only structured data (e.g. vibration signals, voltage signals, and acoustic emission signals), not unstructured data (e.g. infrared images), which provides another perspective on the mechanical health condition. To apply multi-sourced heterogeneous monitoring data fully, this paper presents a novel fusion diagnosis method integrating structured and unstructured data for rotor system faults. A novel multi-mode convolutional neural network (M-CNN) is first proposed to automatically learn fault-sensitive features from raw multisensory data composed of vibration signals and infrared images. M-CNN equipped with adjustable filter banks can identify data types and adaptively adopt the appropriate convolution mode. Then, t-distributed stochastic neighbor embedding (t-SNE) is introduced to fuse the deep features to further improve the quality of the learned features. Finally, the fused features are employed to conduct fault classification tasks. The effectiveness of the scheme is verified by fault diagnosis experiments in a rotor system, where it achieves a remarkable classification rate of 98.97%. Compared with similar methods, the proposed method exhibits outstanding performance, indicating the feasibility of using multi-sourced heterogeneous data for rotor system fault-diagnosis.