AUTOMATIC MACHINE FAULT DIAGNOSIS BASED ON WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORKS

Machinery parts always put imprint on the product during the production processing.
Industry in the developing country the discovery of defects depends on the human experience and
spectrum analysis (SA). Fast Fourier Transform (FFT) is basis for SA and uses to extract the frequency
features which help the experts to identifying the causes of defected parts in machine. In this paper
presents a new technique to automatic fault diagnosis. The proposed technique is constituted of two
stages architecture: the first stage is analysis the product signals to extract the features by using
wavelet transform (WT). The second stage is devoted to the classification of defect from the features by
using probabilistic neural network (PNN). Naïve Bayesian algorithm and Bayesian net algorithm is
taken for classification and compared. The novelty of the proposed method resides in the ability not
only with higher precision, but also with dimensionality reduction and higher speed than method of
Fourier transform and mathematical statistics.