Farthest-Nearest Distance Neighborhood and Locality Projections Integrated With Bootstrap for Industrial Process Fault Diagnosis

Abstract

It has become a big challenge and a hot topic of research to capture the most relevant features from high-dimensional process data for enhancing fault diagnosis. To effectively extract discriminative features from high-dimensional data, a novel dimensionality reduction (DR) approach named neighborhood and locality projections with the farthest and nearest distance (FNDNLP) is first proposed for industrial process fault feature acquisition and diagnosis. By constructing intra-class weights and inter-class weights, FNDNLP takes both the intra-class distance and the inter-class distance into consideration in its objective function, improving the diagnostic ability of extracted features through maximizing the inter-class distance and minimizing the intra-class distance. In addition, bootstrap based FNDNLP (BFNDNLP) is further proposed to handle the matrix decomposition problem in FNDNLP. To find the proper order through DR, the Akaike information criterion (AIC) is adopted. Finally, the Naïve Bayes (NB) based classifier is utilized to achieve acceptable fault diagnosis. The simulation results from two complex industrial cases indicate that the proposed methodology can achieve higher diagnosis accuracy than other related methods. What's more, the DR features are further analyzed to show the effectiveness and benefits of the proposed BFNDNLP extraction approach. The main source code of this paper can be obtained on GitHub soon.