A Fourier descriptor and PSCS-RBF fusion method for pumping machine fault diagnosis

Abstract

Abstract For the current oilfield pumping machine fault diagnosis, there are time-consuming and inefficient problems, and at the same time high requirements for hardware resources and no universality, this study proposes a pumping machine fault diagnosis method based on improved Fourier descriptor combined with dynamic adaptive cuckoo search (PS cuckoo search, PSCS) to optimize RBF neural network. Firstly, the starting point position of the contour of the power diagram is determined by the minimum inertia axis, and the Fourier transform is performed to achieve the best matching between contours, and the effect of starting point irrelevance. Then feature extraction is performed by combining shape invariant moments as the input layer information of RBF neural network. Then dynamic discovery probability and adaptive step size are introduced to make the cuckoo search easier to retain the better solution, and the step size can be automatically adjusted according to the convergence rate of the objective function to maintain a balanced state of efficiency and accuracy in different search stages. Finally, the RBF neural network is optimized by the improved cuckoo search to obtain the optimal relevant parameters such as the width and weights of RBF, and the PSCS-RBF fault diagnosis model is established. The model is applied to the diagnosis of different fault types of pumping machines and is compared and analyzed with a variety of current mainstream models. The average detection accuracy of the PSCS-RBF fault diagnosis method proposed in the article reaches 96.3%, and the measured results have high accuracy and short time, which verifies the practical value and advantages of the method.