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

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 intraclass weights and interclass weights, FNDNLP takes both the intraclass distance and the interclass distance into consideration in its objective function, improving the diagnostic ability of extracted features through maximizing the interclass distance, and minimizing the intraclass 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 is adopted. Finally, the Naïve Bayes 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 is more, the DR features are further analyzed to show the effectiveness and benefits of the proposed BFNDNLP extraction approach.