Early Detection of Network Fault Using Improved Gray Wolf Optimization and Wavelet Neural Network

Abstract

To address the problem of diagnostic accuracy and stability degradation caused by random selection of the initial parameters for the wavelet neural network (WNN) fault diagnosis model, this paper proposes a network troubleshooting model based on the improved gray wolf algorithm (IGWO) and the wavelet neural network. First, the convergence factor and policy for the weight update are redesigned in the IGWO algorithm. This study uses a nonlinear convergence factor to balance the global and local search capabilities of the algorithm and dynamically adjusts the weights according to the adaptability of the head wolf α to strengthen its leadership position. Thereafter, the initial weights and biases of the WNN are optimized using the IGWO algorithm. During the backpropagation of the WNN error, momentum factors are introduced to prevent the model from falling into local optimization. Experimental results show that the IGWO algorithm is far better than GWO in terms of convergence speed and convergence accuracy. Furthermore, the average diagnostic accuracy of the IGWO-WNN model on the KDD-CUP99 dataset reaches 99.22%, which is 1.15% higher than that of the WNN model, and the stability of the diagnostic results is significantly improved.

Method of aeroengine fault diagnosis based on a Fingerprint map

“…3 The data-based fault diagnostic method does not need an accurate mathematical mechanism model but only needs to make full use of the knowledge, experience of experts and existing data in the engine field. It includes many classic and typical machine learning methods, such as artificial neural networks (ANNs), [4][5][6][7] extreme learning machines (ELMs), 8 kernel recursive least squares (KRLS), support vector machines (SVMs) and so on. [9][10][11][12] Montazeri-Gh et al proposed a novel approach based on learning the fault characteristic maps of gas turbine components using an ELM.…”