A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based D-statistic and DV-contribution plots

This paper presents an entire sensor fault detection and diagnosis (FDD) for screw chiller system using the support vector data description (SVDD) algorithm. It has advantages of solving problems on describing non-linear and non-Gaussian distributed data. A distance-based D-statistic plot is employed to detect sensor faults. Based on the distance transformation in mathematical way, a new distance variation-based DV-contribution plot is proposed to diagnose the sensor fault. The screw chiller field measured data is utilized to train the SVDD model via a hybrid parameter tuning approach combined the grid search and the 10-fold cross validation. Six typical sensor faults are introduced for validation, i.e. positive and negative biases, positive and negative drifts, precision degradation and complete failure. Test results of both D-statistic and DV-contribution plots show that the SVDD-based method has good FDD results for the six sensor faults. Furthermore, the proposed DV-contribution plot shows more accurate fault diagnosis results compared with the principal component analysis (PCA)-based Q-contribution plot.