1. 基于改进小波包结合CS-BP的地面驱动螺杆泵故障诊断.
- Author
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李博文, 宋文广, 徐加军, and 张宝
- Abstract
Aiming at the problems of low efficiency, low precision and resource loss in the fault diagnosis of ground-driven screw pump, a diagnosis method of back propagation(BP) neural network optimized by introducing the idea of power spectrum refinement was proposed to improve wavelet packet transform and cuckoo search(CS). Firstly, the active power of the screw pump was decomposed and reconstructed by the improved wavelet transform to obtain the feature vector. Secondly, it was normalized with the parameters such as instantaneous flow and inlet back pressure as the output layer information of the BP neural network. Thirdly, the weights and thresholds of BP neural network were obtained by CS, and the CS-BP fault diagnosis model was established. Finally, it was applied to the diagnosis of different fault types of screw pump, and the diagnosis effect was analyzed and compared with the current mainstream diagnosis methods. The results show that the average accuracy of fault diagnosis for different types of screw pump is 95. 6%, and the feasibility and superiority of the proposed method are proved by comparative analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2023