1 research outputs found
Multivariate statistical process monitoring
U industrijskoj proizvodnji prisutan je stalni rast zahtjeva, u prvom redu, u pogledu ekonomiÄnosti proizvodnje, kvalitete proizvoda, stupnja sigurnosti i zaÅ”tite okoliÅ”a. Put ka ispunjenju ovih zahtjeva vodi kroz uvoÄenje sve složenijih sustava automatskog upravljanja, Å”to ima za posljedicu mjerenje sve veÄeg broja procesnih veliÄina i sve složenije mjerne sustave. Osnova za kvalitetno voÄenje procesa je kvalitetno i pouzdano mjerenje procesnih veliÄina. Kvar na procesnoj opremi može znaÄajno naruÅ”iti proizvodni proces, pa Äak prouzrokovati ispad proizvodnje Å”to rezultira visokim dodatnim troÅ”kovima. U ovom radu se analizira naÄin automatskog otkrivanja kvara i identifikacije mjesta kvara u procesnoj mjernoj opremi, tj. senzorima. U ovom smislu mogu poslužiti razliÄite statistiÄke metode kojima se analiziraju podaci koji pristižu iz mjernog sustava. U radu se PCA i ICA metode koriste za modeliranje odnosa meÄu procesnim veliÄinama, dok se za otkrivanje nastanka kvara koriste Hotellingova (T**2), I**2 i Q (SPE) statistike jer omoguÄuju otkrivanje neobiÄnih varijabilnosti unutar i izvan normalnog radnog podruÄja procesa. Za identifikaciju mjesta (uzroka) kvara koriste se dijagrami doprinosa. Izvedeni algoritmi statistiÄkog nadzora procesa temeljeni na PCA metodi i ICA metodi primijenjeni su na dva procesa razliÄite složenosti te je usporeÄena njihova sposobnost otkrivanja kvara.Demands regarding production efficiency, product quality, safety levels and environment protection are continuously increasing in the process industry. The way to accomplish these demands is to introduce ever more complex automatic control systems which require more process variables to be measured and more advanced measurement systems. Quality and reliable measurements of process variables are the basis for the quality process control. Process equipment failures can significantly deteriorate production process and even cause production outage, resulting in high additional costs. This paper analyzes automatic fault detection and identification of process measurement equipment, i.e. sensors. Different statistical methods can be used for this purpose in a way that continuously acquired measurements are analyzed by these methods. In this paper, PCA and ICA methods are used for relationship modelling which exists between process variables while Hotelling\u27s (T**2), I**2 and Q (SPE) statistics are used for fault detection because they provide an indication of unusual variability within and outside normal process workspace. Contribution plots are used for fault identification. The algorithms for the statistical process monitoring based on PCA and ICA methods are derived and applied to the two processes of different complexity. Apart from that, their fault detection ability is mutually compared