地点
正规化(语言学)
阿卡克信息准则
非线性系统
计算机科学
降维
算法
奇异值分解
维数之咒
数学
数据挖掘
人工智能
模式识别(心理学)
数学优化
机器学习
哲学
语言学
物理
量子力学
作者
Ning Zhang,Yuan Xu,Qunxiong Zhu,Yan‐Lin He
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-10-01
卷期号:19 (10): 10478-10488
被引量:7
标识
DOI:10.1109/tii.2023.3240755
摘要
Data-driven fault diagnosis has attracted attention with the recent trend of obtaining representative features from high-dimensional, strongly coupled, and nonlinear process data. This article presents a novel dimensionality reduction (DR) algorithm named double preserving integrated with neighborhood locality projections (DPNLP) for fault diagnosis. To further solve the singular matrix problem in DPNLP, the regularization-based DPNLP (RDPNLP) that introduces the regularization into DPNLP is finally presented. In RDPNLP, first, the double preserving weight that can both preserve neighborhood similarity and preserve local linear reconstruction is utilized to make the neighbors in the same class close to each other and the neighbors from different classes far apart. Additionally, regularization is applied to solve the singular matrix problem enhancing the ability of DR. Akaike information criterion is utilized to determine the order of DR when using RDPNLP. Through simulations on two compound multifault cases, it can demonstrate that the presented RDPNLP could achieve higher performance in fault diagnosis than other related methods.
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