| Popis: |
The data from which performance models are identified may originate either from planned experiments or from non-intrusive (or observational) data gathered while the system is in normal operation. A large body of well accepted practices is available for the former which falls under the general terminology of “Design of Experiments” (DOE). This is the process of defining the structural framework, i.e., prescribing the exact manner in which samples for testing need to be selected, and the conditions and sequence under which the testing needs to be performed. This would provide the “richness” in the data set necessary for statistically sound performance models to be identified between the response variable and the several categorical factors. Experimental design methods, which allow extending hypothesis testing to multiple variables as well as identifying sound performance models, are presented. Selected experimental design methods are discussed such as randomized block, Latin Squares and 2k factorial designs. The parallel between model building in a DOE framework and linear multiple regression is illustrated. Finally, this chapter addresses response surface methods (RSM) which allow accelerating the search towards optimizing a process or towards finding the conditions under which a desirable behavior of a product is optimized. RSM is a sequential approach where one starts with test conditions in a plausible area of the search space, analyzes test results to determine the optimal direction to move, performs a second set of test conditions, and so on till the required optimum is reached. |
