Machines, Год журнала: 2024, Номер 12(12), С. 921 - 921

Опубликована: Дек. 16, 2024

The rotating pump of pipelines are susceptible to damage based on extended operations in a complex environment high temperature and pressure, which leads abnormal vibrations noises. Currently, the method for detecting conditions pumps primarily involves identifying their sounds vibrations. Due background noise, performance condition monitoring is unsatisfactory. To overcome this issue, pipeline proposed by extracting fusing sound vibration features different ways. Firstly, hand-crafted feature set established from two aspects vibration. Moreover, convolutional neural network (CNN)-derived one-dimensional CNN (1D CNN). For CNN-derived sets, selection presented significant ranking according importance, calculated ReliefF random forest score. Finally, applied at level. According signals obtained experimental platform, was evaluated, showing an average accuracy 93.27% conditions. effectiveness superiority manifested through comparison ablation experiments.

Язык: Английский