Fault Detection and Diagnosis using Principal Component Analysis of Literature review

Fault Detection and Diagnosis using Principal Component Analysis of trembling Data from a Reciprocating Compressor - Literature review ExamplePCA has been employed with genetic algorithms (GA) in battle array to slim information dimensionality for use in open frame diagnosis of induction motors. PCA was employed to remove relative features, aft(prenominal) which GA was employed to select the irrelative features and to optimise the ANN (Yang, Han and Yin, 2006). Fault detection and diagnosis of plant subsystems have too been attempted using PCA. Normal plant operation decomposed through PCA was comp ard to teddyy operation data through PCA decomposition to create thresholds for taking corrective actions. Real time monitoring of plant operation data was comp ared to both data sets with thresholds settled through Q statistics in order to detect faults (Villegas, Fuente and Rodriguez, 2010). Vibration monitoring of eggwhisk transmissions has been attempted using tri-axial acce lerometers and PCA processing of the obtained data. The three different dimensions of acceleration data obtained using accelerometers were reduced to a single dimension using PCA for simpler processing. This set about is seen to provide a simpler and computationally sturdy technique for tingle monitoring in highly complex systems (Tumer and Huff, 2002). Independent PCA models suffer due to the control limits required for the Q and T2 statistics. Also, the limits are produced assuming that the process data is Gaussian in character, which may lead to complications if the process data is non actually Gaussian in character. Probabilistic techniques have been used in conjunction with PCA (PPCA) in order to handle both Gaussian and non-Gaussian process data for fault detection and diagnosis in a process control environment. Outcomes signified improvement all over simple PCA based control schemes, but accredited areas still required improvement under the PPCA based control scheme (He et al., 2012). PCA applications to process control are growing over time. Polyester film process monitoring has been attempted using Q and T2 statistics through a PCA approach for multivariate quality control (MQC). When compared to other techniques, PCA provided a more robust model for fault detection although diagnosis was not highly reliable. It could be inferred that PCA standalone approaches are best suited to fault detection since fault diagnosis requires the application of other techniques for established reliability (Qin, 2003). A combined index consisting of statistics Q and T2 has been developed in order to minimise the index when faulty variables are being isolated. This provides a better solution than applying the received approach of using statistics Q and T2 separately (Chen, Lee and Liu, 2011). It must be noted that PCA provides a simple reducing of dimensionality, but PCA processing is not suited to data streams with a large amount of outliers. A robust PCA (ROBPCA ) method has been suggested for dealing with large dimension data using projection pursuit in junto with robust estimation of lower dimensions. Classification of outliers has been made possible through diagnostic plots (Hubert and Engelen, 2004). ROBPCA has been employed for fault detection and isolation in various theoretical situations in order to prove its worth over PCA. The findings signify that ROBPCA provides better results than PCA with its inherent ability to process large data sets (Tharrault et al., 2008). PCA has also been applied in concert with acoustic emission testing (AET) to deal with vibration monitoring of