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 l_2,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, l_2,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.

中文翻译：

---

不同数据分布下基于2D-gcForest和L${}_{\text{2,p}}$-PCA的智能故障诊断方法

基于深度学习的智能诊断可以揭示运行设备的健康状况，正受到越来越多的工业系统的关注。然而，两个挑战，即深度模型的构建和不同数据分布的适应，限制了这些方法的有效应用。为了弥补这些差距，本文提出了一种基于 2-D-gcForest 和l _{2,p的智能诊断方法}-PCA。首先，在 gcForest 模型之前采用 2-D 采样策略将原始 1-D 序列数据转换为 2-D 堆叠数据，从而减少冗余信息量和计算负担。然后，使用 gcForest 作为基本诊断模型，通过自动扩展层来学习具有简单超参数设置的数据特征。同时，结合l _2,p -PCA 优化转换后的特征，改善不同数据源的特征表示。最后通过对比实验验证了所提方法的有效性和优越性。