Mohamed Afizal Mohamed Amin creator text theses my Kuantan, Pahang UMP 2009 monographic eng

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51 p. : ill. (some col.) ; 30 cm. + 1 computer disc Statistical Process Control (SPC) technique has been widely develops for fault detection, diagnosis and control tool. Today, the industries have to keep sustainable production and operate as fault free as possible because faults that present in a process operation increase the operating cost due to products with undesired specifications, malfunction of plant equipment and instrumentation. Therefore, this study is conducted to introduce Statistical Process Control method for detecting fault early enough, so that the corrective action can be taken before the process is upset or out of control. For this research, the historical data at normal operating condition is collected by using Air Flow Pressure Temperature (AFPT) Pilot Plant. The generate data then will be ensure distribute normally before further analysis is carried out. Shewhart individual chart and Shewhart range chart are use to facilitate the fault detected. Based on the result, the Shewhart individual capabilities is more precise estimate of the process standard deviation compare to Shewhart range because it has a smaller uncertainty. Besides that, the computation of Shewhart individual involves all the measurements in each sample, while the computation of Shewhart range involves only two measurements (the largest and the smallest). Based on the result obtained, it shows that both Shewhart range and Shewhart individual chart, can detect fault for both process variables (Temperature and Pressure) and quality variables (Density). After the correlation coefficient is determined it show that the gap between UCL and LCL with CL become wider and make the usage of this technique in Shewhart chart for fault detection gives the best for it has the highest fault detection efficiency.-Author- specialized Mohamed Afizal Mohamed Amin Project paper (Bachelor of of Chemical engineering) -- Universiti Malaysia Pahang - 2009 Bibliography : p. 38-39 Chemical proses control Statistical methods Operation research THE0003776(Local) UMP 100205 20251114204421.0 vtls000045088