Intelligent Fault Diagnosis of Rolling Bearing Based on Optimized Complementary Capability Features and RBF Neural Network by Using the Bees Algorithm

Rolling element bearings are critical mechanical components in rotating machinery. Fault detection and diagnosis in the early stages of damage are 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. In this paper, an efficient method is proposed to extract optimizing features. The method employs capability features as well as the Bees Algorithm to obtain faults detection accurately and separably. This work presents an algorithm using optimum radial basis neural network by the use of the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. Optimum complementary capability values extracted from time-domain vibration signals are used as input features for the neural network. Optimum radial basis trained neural network are able to classify different states of the bearing with 100% accuracy. The proposed procedure requires only a few input features. Effectiveness of the proposed method is illustrated using the bearing vibration data obtained experimentally.