Novel Regularization Double Preserving Integrated With Neighborhood Locality Projections for Fault Diagnosis
Ning Zhang, Yuan Xu, Qunxiong Zhu, Yan‐Lin He
Abstract
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.