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Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor
Engineering and Construction
Pages 5 (1255 words)
Fault Detection Using Q and T2 Statistics PCA is highly effective in reducing the overall dimensions of varied input data for analysis. Over time, PCA has been adopted for use in different applications such as fault monitoring and diagnosis, signal processing, recognition of patterns, data compression and other similar tasks (Zhu, Bai and Yang, 2010).
PCA has been employed with genetic algorithms (GA) in order to reduce data dimensionality for use in fault diagnosis of induction motors. PCA was employed to remove relative features, after 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 also been attempted using PCA. Normal plant operation decomposed through PCA was compared to faulty operation data through PCA decomposition to create thresholds for taking corrective actions. Real time monitoring of plant operation data was compared to both data sets with thresholds settled through Q statistics in order to detect faults (Villegas, Fuente and Rodriguez, 2010). Vibration monitoring of helicopter transmissions has been attempted using tri-axial accelerometers 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 approach is seen to provide a simpler and computationally robust technique for vibration 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. ...
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