Data imbalance is a common problem in rotating machinery fault diagnosis. Traditional data-driven diagnosis methods, which learn fault features based on balance dataset, would be significantly affected by imbalanced data. In this paper, a novel imbalanced data related fault diagnosis method named deep balanced cascade forest is proposed to solve this problem. Deep balanced cascade forest is a multi-channel cascade forest, in which, each of its channels adaptively generates deep cascade structure and is trained on independent data. To enhance the performance of imbalance classification, the deep balanced cascade forest is promoted from both aspects of resampling and algorithm design. A hybrid sampling method, namely Up–down Sampling, is proposed to provide rebalanced data for each cascade forest channel. Meanwhile, a new type of balanced forest with an improved balanced information entropy for attribute selection is designed as the basic classifier of cascade forest. The good synergy of these two methods is the key to the deep balanced cascade forest model. This good synergy makes deep balanced cascade forest achieve the fusion of data-level methods and algorithm-level methods. Comparative experiments on sufficient imbalanced datasets have been designed to verify the performance of the proposed model, and results confirm that deep balanced cascade forest is much more stable and effective in handling imbalance fault diagnosis problem compared to the popular deep learning methods.

数据不平衡是旋转机械故障诊断中的常见问题。传统的基于平衡数据集学习故障特征的数据驱动诊断方法会受到不平衡数据的显着影响。为了解决这个问题，本文提出了一种新的不平衡数据相关故障诊断方法，称为深度平衡级联森林。深度平衡级联森林是一种多通道级联森林，其中每个通道自适应地生成深度级联结构，并在独立数据上进行训练。为了提高不平衡分类的性能，从重采样和算法设计两个方面推进了深度平衡级联森林。提出了一种混合采样方法，即上下采样，为每个级联森林通道提供重新平衡的数据。同时，设计了一种改进了用于属性选择的平衡信息熵的新型平衡森林作为级联森林的基本分类器。这两种方法的良好协同是深度平衡级联森林模型的关键。这种良好的协同作用使得深度平衡级联森林实现了数据级方法和算法级方法的融合。设计了足够多的不平衡数据集的比较实验来验证所提出模型的性能，结果证实，与流行的深度学习方法相比，深度平衡级联森林在处理不平衡故障诊断问题上更​​加稳定和有效。这两种方法的良好协同是深度平衡级联森林模型的关键。这种良好的协同作用使得深度平衡级联森林实现了数据级方法和算法级方法的融合。设计了足够多的不平衡数据集的比较实验来验证所提出模型的性能，结果证实，与流行的深度学习方法相比，深度平衡级联森林在处理不平衡故障诊断问题上更​​加稳定和有效。这两种方法的良好协同是深度平衡级联森林模型的关键。这种良好的协同作用使得深度平衡级联森林实现了数据级方法和算法级方法的融合。设计了足够多的不平衡数据集的比较实验来验证所提出模型的性能，结果证实，与流行的深度学习方法相比，深度平衡级联森林在处理不平衡故障诊断问题上更​​加稳定和有效。