Multi-input parallel graph neural network for semi-supervised rolling bearing fault diagnosis

Abstract Rolling bearing fault diagnosis is the key technology to ensure the reliable, efficient and sustainable operation of rotating machinery. Many fault diagnosis methods have been proposed based on vibration signal analysis from the perspective of data-driven analytics. However, these methods normally take signals of multiple sensors as a whole for feature extraction without considering the relationship among samples. This drawback leads to insufficient feature mining, thereby affecting the accuracy of fault diagnosis. Moreover, these methods need large numbers of labeled samples to achieve high diagnosis accuracy, which requires extensive human labor and is impractical in many real-world applications. To address these issues, a semi-supervised rolling bearing fault diagnosis method based on multi-input parallel graph neural network is proposed in this paper. In the proposed model, signals of multiple sensors are treated separately; thus, features will be extracted parallelly in a more sufficient way. Then, signals of each sensor are constructed into a graph based on limited-radius nearest neighbor, which will add extra relationship information to aid in fault diagnosis. In addition, with the implementation of graph convolutional neural network, the proposed method is able to achieve a more accurate diagnosis than the comparison methods in the case of few labeled data. Finally, the proposed model is evaluated on rolling bearing dataset provided by Case Western Reserve University. Compared with some classical fault diagnosis methods, the proposed model can improve the diagnosis accuracy up to more than 99% even when the proportion of training samples is only 20%.