Abstract: A Monte Carlo General Solution Algorithm is presented. This method is able to solve the problems such as mistaken and multiple solutions in the engine fault diagnosis algorithm based on fault equation, which result from the multi-collinearity of the fault equation. The first part of the paper provides a Monte Carlo algorithm for resolving engine fault equation, which consists of selecting every possible primary factor subset, resolving the sub-equation by using a random simulation method, isolating the reasonable solutions by using a fault correlation criterion, and selecting the independent solutions by using a similarity criterion. This method can ensure to obtain all reasonable solutions meeting the fault equation, can be applied for various fault correlation criteria (such as various residual vector norm criteria) and underdetermined fault equation, and the algorithm is very simple and facile, not necessary to compile specific program for every particular condition. The second part presents the concepts of basic and general solutions, and two methods to obtain them are given by using the principal component analysis and the principal factor model. The complicated diagnostic results caused by multi-collinearity can be transformed into a simpler and clearer form by using the basic and general solutions, which would facilitate to make correct diagnostic decision. A diagnostic effectiveness analysis of the algorithms is presented by using computer simulation, and a test of JT9D practical fault samples is made. The rate of correct diagnosis is up to 86%~92% for the 24 JT9D gas path fault samples.

Key words: aero-engine, condit ion monito ring, fault diagno sis, Monte Carlo method