Author(s)

M.G. Madden & P.J. Nolan

Abstract

Fault tree analysis is widely used in industry in fault diagnosis. The diagnosis of incipient or 'soft' faults is considerably more difficult than of 'hard' faults, which is the situation considered normally. A detailed fault tree model reflecting signal variations over wide range is required for diagnosing such soft faults. This paper describes the investigation of a machine learning method for the automatic generation of fault trees for incipient faults. Features based on the FFT (Fast Fourier Transform) of the time response simulations are used are used to pro- vide a training set of examples comprising records of fault types, severity and fea- ture list. The algorithm presented, called IFT, is derived from the ID3 algorithm for the induction of decision trees. A significant aspect of this approach is that it does not require any detaile

Keywords