A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment
In this paper, an up-to-date overview is provided on the data driven-based fault diagnosis (FD) and remaining useful life (RUL) prediction problems of the petroleum machinery and equipment (PME). First, the FD and RUL prediction of five key components including bearings, gears, motors, pumps and pip...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Taylor & Francis Group
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/aaac3d3ca8484b25b0a3cf91b59315c7 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | In this paper, an up-to-date overview is provided on the data driven-based fault diagnosis (FD) and remaining useful life (RUL) prediction problems of the petroleum machinery and equipment (PME). First, the FD and RUL prediction of five key components including bearings, gears, motors, pumps and pipelines are discussed by adopting mathematical statistics and shallow learning. Then, four kinds of widely-used DL models, i.e. deep neural networks, deep belief networks, convolution neural networks and recurrent neural networks, are surveyed, and the applications in the field of PME are highlighted. Finally, the possible challenges are proposed and some corresponding research directions in the future are presented. |
|---|