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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. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a self adaptive chiller sensor fault detection strategy based on Principal Component Analysis (PCA) method, namely a self-Adaptive Principal Component Analysis (APCA) method. The original data set used to train the PCA model usually contains some error samples, whose useless residual subspace information make the threshold of Q-statistic higher than the expected threshold. This leads to a low sensitivity and low fault detection efficiency at low sensor fault levels. APCA is developed to automatically remove error samples in the original data set in order to improve fault detection efficiency especially for temperature sensor faults with absolute magnitude less than 1°C. The self adaptive process of APCA has been presented and been compared with the Normal Principal Component Analysis (NPCA) method. The APCA strategy is validated by the operational data of a screw chiller system in a real electric factory. The results show that the APCA method can significantly enhance the fault detection efficiency, and the symmetry for positive and negative fault levels with same absolute magnitude becomes better than NPCA method. [Copyright &y& Elsevier]

Sensor faults of air conditioning systems are harmful to optimal control strategies and system performance resulting in poor control of the indoor environment and waste of energy. In order to improve the fault detection and diagnosis (FDD) performance, this paper presents an enhanced sensor fault detection and diagnosis method based on the Satizky-Golay (SG) method and principal component analysis (PCA) method for the VRF system, namely SG-PCA method. Due to the volatility of the original data set of VRF system, the original data are smoothed using SG method at first. Then, the smoothed data are used for PCA model training and fault detection and diagnosis. In order to determine parameters of the SG method, an optimization index is proposed, which is calculated by the signal to noise ratio (SNR), the standard deviation (SD) and the self-detection efficiency. This SG-PCA method for VRF system sensor FDD is validated using field operation data of the VRF system. Various sensor faults at different fault levels are introduced. The results have showed that the SG-PCA method can significantly improve the fault detection and diagnosis performance compared to conventional PCA method. [ABSTRACT FROM AUTHOR]

Detecting the faults at the incipient stage is important for keeping chiller systems healthy and saving energy and maintenance cost. Traditional principle component analysis (PCA) and support vector data description (SVDD) methods are insensitive to two common faults, condenser fouling (CdF) and refrigerant leakage (RfL). To improve the fault detection performance, this study proposed a PCA-R-SVDD based method. Instead of principle component subspace (PCs), it develops a SVDD model in the residual subspace (Rs) using the PCA modeling residual data. The SVDD based distance based monitoring statistic was used for fault detection. The proposed method shows significant improvement comparing with the traditional methods due to the better fault data distribution and tighter monitoring statistic. It is sensitive to six common faults. At least 50% of the fault data can be correctly detected even at the least severe fault level. Centrifugal chiller experimental data from the ASHRAE Research Project 1043 (RP-1043) was used to evaluate the methods. [ABSTRACT FROM AUTHOR]

• The proposed strategy aims at system- and component-level fault types of VRF systems. • An infrequent fault type of VRF systems is studied. • The FDD strategy is based on hybrid data mining methods. • The results are explained and supported by thermodynamics theory. • The established models can accurately detect and diagnose faults of VRF systems. VRF systems are always vulnerable to kinds of faults. Fault detection and diagnosis research should not only accurately identify these faults, but also be capable of obtaining explanations and support in thermodynamic theory. In this study, a strategy is proposed for four types of VRF system faults, including system-level and component-level. The strategy is based on hybrid data mining methods and analyzes the thermodynamic interpretation of the results at the same time. The first preprocessing step eliminates the effect of noise caused by defrosting action in heating mode. We apply unsupervised principal component analysis for feature extraction to reduce the dimensions of data sets. The correlation between principal components and features are investigated. Supervised Gauss naïve Bayes is used to establish the fault detection model with an accuracy of 98.6%. Besides, infrequent fault type is often difficult to be studied because of lacking sufficient data. Therefore, RUSBoost algorithm is used to solve the unbalanced set problem, and the results show enough competitiveness in the comparison of similar algorithms and online testing. Conclusive remarks confirm the truth that the proposed strategy enjoys high versatility, accuracy, and robustness. [ABSTRACT FROM AUTHOR]