Probability estimation algorithms for self validating sensors

摘要

Three alternative approaches are investigated for probability estimation for use in a self validating sensor. The three methods are Stochastic Approximation (SA), a Reduced Bias Estimate (RBE) of this same approach and a method based on the Bayesian Self-Organising Map using Gaussian Kernels (GK). Simulation studies show that the GK-based method gives superior results when compared to the RBE algorithm. It has also been demonstrated that the GK method is more computationally efficient and requires storage space for fewer variables. The techniques are demonstrated using data from a thermocouple sensor experiencing a change in time constant.