计算机科学
规范(哲学)
断层(地质)
矩阵范数
特征选择
理论(学习稳定性)
控制理论(社会学)
人工智能
算法
模式识别(心理学)
机器学习
地质学
控制(管理)
法学
地震学
特征向量
物理
量子力学
政治学
作者
Guowei Zhang,Baokun Han,Shunming Li,Jinrui Wang,Xiaoyu Wang
标识
DOI:10.1088/1361-6501/abf3fb
摘要
In recent times, machine learning has shown its efficiency in the field of fault diagnosis. Nevertheless, in many real-world applications, the basic data are often collected under the condition of machine working condition change, thereby leading to large distribution divergences. Thus, we propose the novel general normalized maximum mean discrepancy (GNMMD) feature-learning method to overcome the limitation of unstable conditions. The proposed algorithm can efficiently handle high-dimensional inputs by enforcing three constraints on the matrix of the learned features, and can optimize the objective function-based generalized norm features and MMD. First, this study analyzes the mapping characteristics of the generalized norm. Second, the feature selection approach based on GNMMD is further studied. Third, the current research also discusses the effects of different choices of norm on the diagnosis performance. Lastly, the data sets of the rolling bearing and planetary gear under unstable conditions are used to verify that the proposed method can achieve superior results.
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