AN INTELLIGENT FAULT DIAGNOSIS APPROACH FOR GEARS AND BEARINGS BASED ON WAVELET TRANSFORM AS A PREPROCESSOR AND ARTIFICIAL NEURAL NETWORKS

Subject Areas : International Journal of Mathematical Modelling & Computations

Mahmuod Akbari ¹ , Hadi Homaei ² , Mohammad Heidari ³

1 - Shahrekord University Iran, Islamic Republic of
2 - Shahrekord University Iran, Islamic Republic of
3 - Islamic Azad University Iran, Islamic Republic of

Received: 2016-04-15 Accepted : 2016-04-15 Published : 2014-03-21

Keywords: Artificial Neural Network, Discrete Wavelet Transform, Vibration analysis, Fault diagnosis,

Abstract :

In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet coefficients of normalized vibration signals has been selected. These features are considered as the feature vector for training purpose of the ANN. A wavelet selection criteria, Maximum Energy to Shannon Entropy ratio, is used to select an appropriate mother wavelet and discrete level, for feature extraction. To ameliorate the algorithm, various ANNs were exploited to optimize the algorithm so as to determine the best values for ‘‘number of neurons in hidden layer” resulted in a high-speed, meticulous three-layer ANN with a small-sized structure. The diagnosis success rate of this ANN was 100% for experimental data set. Some experimental set of data has been used to verify the effectiveness and accuracy of the proposed method. To develop this method in general fault diagnosis application, three different examples were investigated in cement industry. In first example a MLP network with well-formed and optimized structure (20:15:7) and remarkable accuracy was presented providing the capability to identify different faults of gears and bearings. In second example a neural network with optimized structure (20:15:4) was presented to identify different faults of bearings and in third example an optimized network (20:15:3) was presented to diagnose different faults of gears. The performance of the neural networks in learning, classifying and general fault diagnosis were found encouraging and can be concluded that neural networks have high potential in condition monitoring of the gears and bearings with various faults.