Rolling bearing has been becoming an important part of human life and work. The working environment of rolling bearing is very complex and variable, which makes it difficult for fault diagnosis and monitor of rolling bearing from raw vibration data. Then, in this paper, a novel multi-feature learning-based extreme learning machine is proposed for rolling bearing fault diagnosis (FL-ELM). Extreme learning machine (ELM) is a fast and generalized algorithm proposed for training single-hidden-layer feed-forward networks (SLFNs), which has fast computing speed and small testing error. The novel architecture has two hidden layers and an experience pool sandwiched between two hidden layers. The first hidden layer consists of multi-feature learning methods. The experience pool is used to sort and choose new data, with old data being filtered out. Firstly, the first hidden layer is adopted for feature extraction. Secondly, the experience pool is used to rearrange and select data, which is extracted by first hidden layer. Thirdly, ELM is employed to further learn and classify. The proposed method (FL-ELM) is applied to the rolling bearing fault diagnosis. The results confirm that the proposed method is more effective than traditional methods and standard deep learning methods.

滚动轴承已成为人类生活和工作的重要组成部分。滚动轴承的工作环境非常复杂多变，难以根据原始振动数据进行滚动轴承的故障诊断和监测。然后，在本文中，提出了一种新的基于多特征学习的极限学习机用于滚动轴承故障诊断（FL-ELM）。极限学习机（ELM）是为训练单隐藏层前馈网络（SLFNs）而提出的一种快速通用的算法，具有计算速度快、测试误差小的特点。这种新颖的架构有两个隐藏层和一个夹在两个隐藏层之间的体验池。第一个隐藏层由多特征学习方法组成。经验池用于排序和选择新数据，过滤掉旧数据。首先，采用第一个隐藏层进行特征提取。其次，经验池用于重新排列和选择由第一隐藏层提取的数据。第三，使用ELM进一步学习和分类。所提出的方法（FL-ELM）应用于滚动轴承故障诊断。结果证实，所提出的方法比传统方法和标准深度学习方法更有效。