Diagnosis using fault trees induced from simulated incipient fault case data

Abstract

This paper presents comprehensive results describing the diagnosis of incipient faults based on fault trees derived using the IFT induction algorithm. The test system is a robot arm controlled by a pneumatic servo-mechanism. Detailed simulations using a nonlinear dynamic model were used to provide a training set of examples. The effectiveness of the diagnosis is demonstrated using comparative results based on a neural network approach