A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method

Abstract

This paper presents a statistical training data cleaning strategy for PCA-based chiller sensor Fault Detection, Diagnosis and Data Reconstruction method. Finding and removing outliers from the original training data set, the training data quality can be improved by the presented data-cleaning strategy. This can enhance the efficiency of the fault detection and increase the accuracy of the data reconstruction. Outliers cannot be easily found in the original data set used for training the PCA model. These outliers would severely affect the projection directions of PCA's two orthogonal subspaces (PC subspace and Residual subspace). Therefore, the threshold of Q-statistic is changed by the unexpected projection subspaces so that the detection efficiency of the sensor fault is decreased. The Euclidean distance was employed as an index to detect outliers from the original training data. In order to achieve optimal training data for the sensor FDDR, the z-scores of each sample's Euclidean Distance were employed as the key to remove the outliers. A field measured data set of a screw water-cooled chiller was used to validate the presented strategy. Results demonstrate that the quality of the training data is optimized and sensor fault detection efficiency, as well as the reconstruction data accuracy, is improved when compared to the normal PCA method.