سال انتشار: ۱۳۹۱
محل انتشار: بیستمین کنفرانس سالانه مهندسی مکانیک
تعداد صفحات: ۴
Behrooz Attaran – Graduate Student, Mechanical Engineering Department, University of Shahid Chamran
Afshin Ghanbarzadeh – Assistant Professor, Mechanical Engineering Department, University of Shahid Chamran
Karim Ansari-Asl – Assistant Professor, Electrical Engineering Department, University of Shahid Chamran
Reza Zaeri – Graduate Student, Mechanical Engineering Department, University of Shahid Chamran
Rolling element bearings are very important mechanical components in rotating machineries. Fault detection and diagnosis in the early stages of damage is necessary to prevent their malfunctioning and failure during operation. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper presents an algorithm using coherence analysis and optimized windowing function and feed-forward network trained with optimized Levenberg-Marquardt by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. Magnitude squared coherences of periodogram are used as input features for the neural network. Trained neural networks are able to classify different states of the bearing with 100% accuracy. The proposed procedure requires only a few input features, resulting in simple preprocessing and faster training. Effectiveness of the proposed method is illustrated using the experimentally obtained bearing vibration data.