초록 ▼

The invention discloses method for self-optimizing, dynamically and in real time, a machine relative to a specific performance characteristic in response to variations on a prescribed number m of variables. This method comprises statistically designing a matrix of operating conditions of the machine

The invention discloses method for self-optimizing, dynamically and in real time, a machine relative to a specific performance characteristic in response to variations on a prescribed number m of variables. This method comprises statistically designing a matrix of operating conditions of the machine; sequentially operating the machine according to the designed matrix of operating conditions; testing the performance characteristic of the thus-operated machine; analyzing the tested performance characteristic against a given optimizing criterion; and feeding back for or returning to the designing step for a new, improved operating condition. A machine in the form of programmed microprocessor embodying this self-optimizing method is also disclosed.

대표청구항 ▼

A method for self-optimizing, in real time, a machine relative to a specific performance characteristic in response to variations on a prescribed number m of variables or parameters comprising: programming the machine to cause it to plan, by itself and without human control and intervention, a stati

A method for self-optimizing, in real time, a machine relative to a specific performance characteristic in response to variations on a prescribed number m of variables or parameters comprising: programming the machine to cause it to plan, by itself and without human control and intervention, a statistical design which prescribes the number n of tests and the exact design matrix in which the test level of each of the m variables is specified for all the n tests; according to the design matrix substantially continuously performing said n designed tests on the very particular machine without relying on extrapolations based on sampled test results obtained on other similar but often different or even irrelevant machines so as to eliminate errors due to sampling and extrapolation from machine to machine, part to part, and time instant to time instant; substantially continuously collecting and analyzing the resultant n sets of test data on the test levels of the m variables and the associated performance characteristic to determine the functional relationship between said variables and said specific performance characteristic; from the functional relationship determining the combination of the conditions of said m variables that gives optimum value for the specific performance characteristic for the particular machine, environment, and time; and setting the conditions or levels of said m variables at their respective, thusdetermined conditions.