Novel Regularization Double Preserving Integrated With Neighborhood Locality Projections for Fault Diagnosis
Novel Regularization Double Preserving Integrated With Neighborhood Locality Projections for Fault Diagnosis
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DOI：
10.1109/tii.2023.3240755
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发表时间：
2023-10
影响因子：
12.3
通讯作者：
Ning Zhang;Yuan Xu;Qun Zhu;Yanlin He
中科院分区：
文献类型：
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作者：
Ning Zhang;Yuan Xu;Qun Zhu;Yanlin He
随着从高维、强耦合、非线性的过程数据中提取具有代表性的特征的趋势，数据驱动的故障诊断引起了人们的关注。提出了一种用于故障诊断的双重保留与邻域局部性投影相结合的降维(DR)算法。为了进一步解决DPNLP中的奇异矩阵问题，将正则化引入到DPNLP中，提出了基于正则化的DPNLP(RDPNLP)。在RDPNLP算法中，首先利用同时保持邻域相似性和保持局部线性重构的双重保留权，使得同一类中的邻域距离较近，而不同类的邻域之间的距离较远。此外，利用正则化方法解决了奇异矩阵问题，提高了Dr.Akaike信息判据在RDPNLP中确定DR阶数的能力。通过对两个复合多故障案例的仿真，结果表明，与其他相关方法相比，RDPNLP具有更高的故障诊断性能。
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.