The invention relates to a method for monitoring health of a rotary machine suitable for a working condition changing condition. The method comprises the steps that (1) a monitor model is constructed, wherein a relevance vector machine is used for fitting the function relation of health characteristic parameters and a working condition, the function relation is used as the parameters of a self-adaption threshold model, and the self-adaption threshold model is constructed; (2) a health state is monitored, wherein through test signals of the rotary machine to be detected, the constructed self-adaption threshold model is used for detecting whether test data exceed a threshold value or not, the machine is judged to be healthy if the test data do not exceed the threshold value, and otherwise, the machine is judged to break down. According to the method, the relevance vector machine is used for fitting the mean value of health characteristics and the function relation of standard deviation changing along with working condition parameters, and the method has the advantages that the relevance vector machine has strong learning capacity, the problems of the local minimum, the over-fitting and the under-fitting of a neural network can be solved, the relevance vector machine has better sparsity than a support vector machine, and obtained results are simpler and more practical. The method has the advantages of being high in monitor precision and capable of being used under rotating speed changing and load changing conditions.