RBF neural network and support vector machine (SVM), two Artificial Intelligent (AI)
methods, have been extensively applied on machinery fault diagnosis. Aero-engine, as one kind of
rotating machine with complex structure and high rotating speed, has complicated vibration faults. As
one kind of AI methods, RBF neural network has the advantages of fast learning, high accuracy and
strong self-adapting ability. Support vector machine, another AI method, only needs a small quantity
of fault data samples to train the classifier and does not need to extract signal features. In this paper,
the applications of two AI methods on aero-engine vibration fault diagnosis are introduced. Firstly,
the principles and algorithm of both two methods are presented. Secondly the fundamentals of
two-shaft aero-engine vibration fault diagnosis are described and gotten the standard fault samples
(training samples) and simulation samples (testing samples). Third, two AI methods are applied to the
vibration fault diagnosis and obtained the diagnostic results. Finally, the advantages and
disadvantages of the two methods are compared such as the computing speed, accuracy of diagnosis
and complexity of algorithm, and given a suggestion of selecting the diagnostic methods.
Aero-engine Vibration Signal Blind Separation Based on BP Neural Network Algorithm