A Novel Effective Dimensionality Reduction Algorithm for Water Chiller Fault Data

Abstract

The reliability of chiller is very important for the safe operation of refrigeration system. In order to solve the problem that the traditional linear discriminant analysis (LDA) based on L2 norm is sensitive to outliers, this paper introduced a novel dimensionality reduction algorithm for chiller fault data set - RSLDA. Firstly., L2,1 norm is used to extract the most discriminant features adaptively and eliminate the redundant features instead of L2 norm. Secondly, an orthogonal matrix and a sparse matrix are introduced to ensure the extracted features contain the main energy of the raw features. In addition., the recognition rate of the nearest classifier is defined as the performance criteria to evaluate the effectiveness of dimensionality reduction. Finally., the reliability of algorithm was verified by experiences compared with other algorithms. Experimental results revealed that RSLDA not only improves robustness but also has a good performance in the Small Sample Size problem (SSS) of fault classification.