Blade fault diagnosis using artificial neural network

Abstract

Fourier and wavelet analysis of vibration signals are the two most commonly used techniques for blade faults diagnosis in turbo-machinery. However, blade faults diagnosis based on visual comparison of vibration spectrum and wavelet maps are very subjective as it required experiences and knowledge to interpret the results. To overcome these challenge, new approaches for blade fault diagnosis based on artificial intelligent vibration analysis need to be devised to achieve a more objective and repeatable blade fault diagnosis. In this study, continuous wavelet transform was used to analyse the vibration signals and its results were subsequently used for feature extraction. Features extracted based on the statistical parameters calculated from the wavelet coefficients were then fed into the artificial neural network (ANN) model for blade faults diagnosis. Results of ANN classification show that the features obtained from the wavelet coefficients achieved classification accuracy of 88.43%. The proposed method can therefore use as an alternative method for blade fault diagnosis.