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国外文献 > 1.轴承 > 7. 智能方法与计算 > 机器学习

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标 题： A BROAD TRANSFER LEARNING ALGORITHM FOR CLASSIFICATION OF BEARING FAULTS
轴承故障分类的广义转移学习算法
作 者： Guokai Liu
Liang Gao
Weiming Shen
Andrew Kusiak
关键词： Fault diagnosis  [故障诊断]
Deep learning  [ 深度学习]
Broad learning  [ 广泛学习]
Domain adaptation  [ 领域自适应]
Transfer learning  [ 迁移学习]
出版年： 2020
文集名： Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality And Reliability
制造工艺； 制造系统； 纳米/微米/中尺度制造； 品质与可靠性
会议名： 15th ASME International Manufacturing Science and Engineering Conference (MSEC 2020)
第15届美国机械工程师学会国际制造科学与工程会议（MSEC 2020）
届 次： 15th
摘 要： Condition monitoring and fault diagnosis are of great interest to the manufacturing industry. Deep learning algorithms have shown promising results in equipment prognostics and health management. However, their success has been hindered by excessive training time. In addition, deep learning algorithms face the domain adaptation dilemma encountered in dynamic application environments. The emerging concept of broad learning addresses the training time and the domain adaptation issue. In this paper, a broad transfer learning algorithm is proposed for the classification of bearing faults. Data of the same frequency is used to construct one- and two-dimensional training data sets to analyze performance of the broad transfer and deep learning algorithms. A broad learning algorithm contains two main layers, an augmented feature layer and a classification layer. The broad learning algorithm with a sparse auto-encoder is employed to extract features. The optimal solution of a redefined cost function with a limited sample size to ten per class in the target domain offers the classifier of broad learning domain adaptation capability. The effectiveness of the proposed algorithm has been demonstrated on a benchmark dataset. Computational experiments have demonstrated superior efficiency and accuracy of the proposed algorithm over the deep learning algorithms tested.

状态监测和故障诊断在制造业中具有重要意义。深度学习算法在设备预测和健康管理方面已展现出良好效果，但其成功受到训练时间过长的制约。此外，深度学习算法还面临动态应用环境中出现的领域适应难题。新兴的广义学习概念正致力于解决训练时间和领域适应问题。本文提出一种广义迁移学习算法，用于轴承故障分类。通过使用相同频率的数据构建一维和二维训练数据集，以分析广义迁移与深度学习算法的性能。该广义学习算法包含两个主要层次：增强特征层和分类层。采用稀疏自编码器提取特征的广义学习算法，结合目标域每类样本数量有限（最多十个）重新定义的成本函数，可获得具备广义学习领域适应能力的分类器。所提出的算法在基准数据集上验证了其有效性。计算实验表明，该算法在效率和准确性方面均优于所测试的深度学习算法。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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