Enhanced Monitoring using Statistical Fault Detection Methods and Applications to Photovoltaic Systems and Genomic Data
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Effective operation of various engineering systems requires tight monitoring of some of their key process variables. For example, detection of anomalies in photovoltaic (PV) power systems is crucial for their efficient application to convert solar energy to usable power. Also, detecting aberrations in genomic data helps the diagnosis of various diseases, such as cancer. Various fault detection techniques have been developed and utilized in practice. For example, statistical fault detection techniques that are based on hypothesis testing, such the generalized likelihood ratio test (GLRT), have been shown to be among the most effective univariate fault detection methods. Most practical processes, however, are multivariate, i.e., involve many variables that need to be monitored at the same time. In a previous research effort, we have developed PCA and kernel PCA (kPCA)-based GLRT fault detection schemes, in which PCA and kPCA have been used as a modeling framework for fault detection. In this project, our objective is three-fold: improve the performance of the GLRT, extend it applicability to a wide range of practical systems, and apply the developed techniques to enhance monitoring PV and biological systems. First, to improve the performance of the GLRT, a new statistical fault detection method, that is based on combining the advantages of the exponentially weighed moving average (EWMA) filter with those of the GLRT, will be developed. The developed method, which is called EWMA-based GLRT, will provide improved properties, such as smaller missed detection and false alarm rates and smaller average run length. The second objective of this project is to extend the applicability of the developed GLR methods to a wide range of practical systems. Most real systems are nonlinear, multivariate, and are best represented by input-output type of models. Latent variable models, such Partial Least Squares (PLS), have been widely used to represent such systems. Therefore, in this project, linear and nonlinear PLS-based GLRT and EWMA-based GLRT methods will be developed to widen the applicability of these techniques in practice. For nonlinear systems, kernel PLS (kPLS), which is capable of dealing with high dimensional nonlinear data, will be used to make such an extension. Also, in most practical situations, fault detection is needed online, i.e., as the data are measured. The nonlinear latent variable models, however, are batch, i.e., they require the entire data sets to be available a priori. Therefore, recursive kPCA and kPLS modeling schemes will be developed to extend the advantages of the GLRT methods for online systems..........
