LAUSR

Intelligent diagnosis based on deep learning can reveal the health status of running equipment and is attracting attention for an increasing number of industrial systems. However, two challenges, namely, the construction of deep models and the accommodation of different data distributions, restrict the effective application of such methods. To bridge these gaps, this article proposes an intelligent diagnostic method based on 2-D-gcForest and l2,p-PCA. First, a 2-D sampling strategy is employed before a gcForest model to transform the raw 1-D sequence data into 2-D stacked data, thus reducing the amount of redundant information and the computational burden. Then, gcForest is used as a basic diagnostic model, which learns data features with simple hyperparameter settings by automatically extending layers. Simultaneously, l2,p-PCA is incorporated to optimize the transformed features, improving the feature representation for different data sources. Finally, comparative experiments are reported to validate the effectiveness and superiority of the proposed method.