… of fault. This paper proposes a deep learning approach with data augmentation for rotating
machinery fault diagnosis. … with data augmentation is proposed for fault diagnosis of rotating …

… In order to validate the effectiveness of the proposed method, two multi-mode fault data
augmentation and intelligent diagnosis cases for bearings and gears are studied in this chapter. …

… data augmentation (DA) and metric learning is proposed for an intelligent bearing fault diagnosis
under limited labeled data… on an experimental bearing fault dataset from our laboratory …

… eg, industrial process data, some more data augment methods are needed… data augmentation
to increase the numbers of input data samples in low-data domain of the imbalanced data …

… on two rotating machinery datasets, the data-driven fault diagnostic model can significantly
… proposed data augmentation method is promising for fault diagnostic tasks with imbalanced …

… role in process monitoring and fault diagnosis. While the online monitoring system uses
sensors to obtain measurement data of mechanical equipment and industrial processes which …

… Deep learning algorithms for diagnosing machinery faults have become prevalent owing to
their robustness and capacity for adaptation. Deep architectures of computational layers and …

… potential for data augmentation and intelligent fault diagnosis of … Li, “Machinery fault diagnosis
with imbalanced data using … framework for rotating machinery fault diagnosis under strong …

… attention of researchers in machinery fault diagnosis. In the light … of final diagnosis accuracy,
data preprocessing is necessary … data augmentation used in the CNN based intelligent fault …

… or feature adaptation without data augmentation. On the … machines can be classified directly
by designing fault classifiers suitable for small & imbalanced data without data augmentation …