Optimized neural network with self-adaptive MHBO algorithm for fault diagnosis

Abstract

Bearing operation affects the usual operations of mechanical equipment. It is of much practical and theoretical concern to perform faults diagnosis in bearing. Accordingly, the extraction and election of faulty features aid in enhancing the accuracy of fault diagnosis. Nevertheless, the analysis endures from “(1) High dimension of the selected features, (2) Uncertainty of single sensor for data sampling.” Thereby, this work develops a fault diagnosis approach that contains two main phases “(1) Feature extraction and (2) Diagnosis.” At first, it extracts the “empirical wavelet transform, empirical mode decomposition, and wavelet transform” based features. Following this, the derived features are given to optimized neural network (NN) for diagnosis. Further, for progressing the diagnosis accuracy of the established scheme, the weights of NN are fine-tuned through self-adaptive marriage in honey bee optimization. At last, the supremacy of the presented approach is proved with respect to varied measures.