Fault detection of bearing using advanced artificial interlligence

Thumbnail Image
Danial Danish Bin Mustaffa Kamal
Journal Title
Journal ISSN
Volume Title
Research Projects
Organizational Units
Journal Issue
Advanced artificial intelligence is the most famous techniques to be used in this age. This approach is very suitable as a predictor for certain problems, since its mechanism and system are sensitive and precise. The main objective of this project is to create a support vector machine model, which is one of the AI techniques to detect and diagnose bearing fault at early stage. The development of the model should be able to forecast the bearing fault diameters based on the collected input variables. In order to achieve this objective, a set of bearing raw vibration frequency signal is acquired. The raw vibration signals is extracted by using Matlab Software. The extracted features are used as the inputs containing different motor loads, different motor speeds and different locations. The support vector machine approach from Statistica Software is being used to run the simulation. The selection of kernel functions and other parameters are very important in the development of a reliable model. Trial and error method is used to identify the best combination of parameters for a SVM model by comparing the simulation results. The best kernel functions and parameters are set and the model is ready to be used to run the real data since it can provide the best and most accurate precision in early detecting bearing failures.
Advanced artificial intelligence , Support vector machine ( SVM) , Bearing failures