Registering classes of system- or process states influencing later processes, employs multi-sensor array in adaptive learning process using neural network or fuzzy logic to define state vectors and classes

摘要

Registering classes of system- or process states influencing later processes, employs multi-sensor array in adaptive learning process using neural network or fuzzy logic to define state vectors and classes. Preferred Features: Sensors of differing and/or the same principles of operation, are arrayed to detect continuously, characteristic parameters of the system under assessment. During an adaptive learning phase defined by the given process cycle, a model of the process is constructed. This is divided into defined classes serving as a scale, and is produced by means of neural networks and/or fuzzy logic. A vector formed from the sensor signals, combines characteristics from the detection and adaptive learning phases. It is examined for its membership of a class, in this way, it is made available as an identifiable magnitude. The information gathered, is used to influence actuators and/or control- and regulation systems. The vector is corrected, as a function of further system characteristics, such as maintenance, plant status, sensor status and data models produced in correspondence with them. An Independent claim is included for equipment to carry out the method. This comprises sensors arrayed to take contact- or non-contact measurements from the relevant media, and a computer.