Artificial Neural Network-based fault diagnostics of an electric motor using vibration monitoring

Author(s):  
Mona Khatami Rad ◽  
Mohammadehsan Torabizadeh ◽  
Amin Noshadi
Author(s):  
Dongmei Du ◽  
Qing He

Orbit is a significant symptom in the fault diagnosis of rotating machine. The orbit is a 2-D image and can be described by moment invariants, the shape property of 2-D image, which is a description with translating-, rotating-, and scaling-invariants for 2-D image. The descriptive method of orbit image is investigated and an automatic orbit shape recognition based on artificial neural network (ANN) with moment invariants is proposed in this paper. The ANN of orbit shape recognition is trained by the training patterns generated by computer simulation for plenty of orbit shapes. It is shown that the trained ANN is of good recognition performance and generalization capability when applied to recognition of the measured orbits. This method can be used to the intelligent expert system of fault diagnosis to obtain automatically online orbit symptom in shafts vibration monitoring of turbine generator, which will improve the automatization of obtaining fault symptom and the automatic diagnosis in the expert system.


2017 ◽  
Vol 37 (4) ◽  
pp. 464-470 ◽  
Author(s):  
Milos Milovancevic ◽  
Vlastimir Nikolic ◽  
Nenad T. Pavlovic ◽  
Aleksandar Veg ◽  
Sanjin Troha

Purpose The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring. Design/methodology/approach As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created. Findings Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment. Originality/value Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.


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