| A novel combination belief rule base model for mechanical equipment fault diagnosis |
| |
| 作者姓名： | Manlin CHEN Zhijie ZHOU Bangcheng ZHANG Guanyu HU You CAO |
| |
| 作者单位： | 1. High-Tech Institute of Xi'an;2. School of Applied Technology, Changchun University of Technology;3. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology |
| |
| 基金项目： | supported by the Natural Science Foundation of China (Nos. 61773388, 61751304, 61833016, 61702142, U1811264 and 61966009)；;China Postdoctoral Science Foundation (No. 2020M673668)； |
| |
| 摘 要： | Due to the excellent performance in complex systems modeling under small samples and uncertainty, Belief Rule Base(BRB) expert system has been widely applied in fault diagnosis. However, the fault diagnosis process for complex mechanical equipment normally needs multiple attributes, which can lead to the rule number explosion problem in BRB, and limit the efficiency and accuracy. To solve this problem, a novel Combination Belief Rule Base(C-BRB) model based on Directed Acyclic Graph(DAG) structu...
|
| 收稿时间： | 1 February 2021 |
A novel combination belief rule base model for mechanical equipment fault diagnosis |
| |
| Manlin CHEN,Zhijie ZHOU,Bangcheng ZHANG,Guanyu HU,You CAO.A novel combination belief rule base model for mechanical equipment fault diagnosis[J].Chinese Journal of Aeronautics,2022,35(5):158-178. |
| |
| Institution: | 1. High-Tech Institute of Xi’ an, Xi’ an 710025, China;2. School of Applied Technology, Changchun University of Technology, Changchun 130000, China;3. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China |
| |
| Abstract: | Due to the excellent performance in complex systems modeling under small samples and uncertainty, Belief Rule Base (BRB) expert system has been widely applied in fault diagnosis. However, the fault diagnosis process for complex mechanical equipment normally needs multiple attributes, which can lead to the rule number explosion problem in BRB, and limit the efficiency and accuracy. To solve this problem, a novel Combination Belief Rule Base (C-BRB) model based on Directed Acyclic Graph (DAG) structure is proposed in this paper. By dispersing numerous attributes into the parallel structure composed of different sub-BRBs, C-BRB can effectively reduce the amount of calculation with acceptable result. At the same time, a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable sub-models. Finally, a fusion method based on Evidential Reasoning (ER) rule is used to combine the belief rules of C-BRB and generate the final results. To illustrate the effectiveness and reliability of the proposed method, a case study of fault diagnosis of rolling bearing is conducted, and the result is compared with other methods. |
| |
| Keywords: | Fault diagnosis Belief rule base Directed acyclic graph Evidential reasoning Mechanical equipment |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
