Nonlinear scaling of signals for intelligent analyzers

Abstract

Operating conditions are often changing so strongly that the changes in nonlinearities must be taken into account. A nonlinear mapping has been developed to extract meanings of variables from measurement signals. The scaling function scale the real values of variables to the range of [-2, +2] which combines normal operation [-1, +1] with handling of warnings and alarms. The nonlinear scaling provides a practical basis for modeling nonlinear multivariable systems with linear methodologies. The approach improves understanding of the meaning of measurement values. The generalized approach is based on a more detailed analysis of the measurement distributions. This can be extended to various applications in digital signal processing.