Diagnostics of life support systems with limited statistical data on failures

Abstract

The authors suggest an approach to determine the technical conditions of life support systems of public buildings in conditions of significant uncertainty of statistical information on failures. To improve the re1iabi1ity and increase the resources of life support systems, maintenance and repair strategies are proposed according to the actual state, which implies the availability of objective diagnostic information. The essence of methods for constructing images of system failures based on training procedures is revealed, the latter being founded on the theory of nonparametric statistical analysis. The image is understood as a formalized description of the failure as an element of the system diagnosis model. The solution of image synthesis problem is given when the orthogonal trigonometric basis is applied in the recurrent relations implementing the learning process. The specific case assumes the existence of data on ranges of diagnostic parameter change at all failures of the investigated object. A modification of the training procedure is performed to build images of failures of life support systems of the latest generation when it is possible to find the ranges of changes in diagnostic parameters only in operational state. The modification consists of the formation and application of an orthonormal binary basis in recurrent relations. There is an example of image constructing of one of the ventilation and air conditioning system failures of a public building on the basis of a modified training procedure.