A self-learning and self-optimizing framework for the fault diagnosis knowledge base in a workshop

Lin，Qi1; Zhang，Yingfeng1,2; Yang，Shangrui1; Ma，Shuaiyin1; Zhang，Tongda3; Xiao，Qinge4

2020-10-01

10.1016/j.rcim.2020.101975

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING   影响因子和分区

0736-5845

1879-2537

The knowledge base is an essential part of the fault diagnosis system, which is crucial to the performance of fault recognition. As the intelligence of the fault diagnosis system has made persistent advance, the increasing demands for diversity and dynamic update have posed challenges to the knowledge base. In this paper, a framework for the fault diagnosis knowledge base is proposed to address the challenges mentioned above. Firstly, a dynamic clustering model is designed using the proposed semi-supervised multi-spatial manifold clustering method to recognize attribute clusters and aggregate new types. When new types are added to this model, it is constantly updated to achieve the automatic evolution of the knowledge base for the diversity of fault. Then, a knowledge evolution model is established by the generative adversarial network algorithm to achieve self-learning and self-optimizing capabilities of the knowledge base. This method learns the distribution of knowledge elements and generates new knowledge elements to optimize the clustering model. Finally, a series of comparative experiments are carried out on bearing datasets to verify the validity of the mentioned framework and models. The comparison results indicate that the proposed method has better performance in fault diagnosis. This research can not only update the knowledge base, but also provide a feasible approach for designing an autonomous knowledge base with self-optimizing and self-learning capabilities.

Data-driven Generative adversarial network Knowledge base Knowledge-driven Self-optimizing Semi-supervised multi-spatial manifold clustering

SCI ; EI

英语

其他

National Natural Science Foundation of China[51675441] ; Fundamental Research Funds for the Central Universities[31020190505001] ; 111 Project Grant of Northwestern Polytechnical University[B13044]

Computer Science ; Engineering ; Robotics

Computer Science, Interdisciplinary Applications ; Engineering, Manufacturing ; Robotics

WOS:000541878200018

PERGAMON-ELSEVIER SCIENCE LTD

20201508389086

Fault detection ; Knowledge based systems ; Cluster analysis ; Generative adversarial networks

Computer Software, Data Handling and Applications:723 ; Artificial Intelligence:723.4 ; Expert Systems:723.4.1

ENGINEERING

2-s2.0-85082727684

Scopus

1.Key Laboratory of Contemporary Design and Integrated Manufacturing Technology,Northwestern Polytechnical University,Shaanxi,710072,China
2.School of Mechanical Engineering,Shaanxi University of Technology,Hanzhong,Shaanxi,723001,China
3.Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Guangdong,518055,China
4.College of Management,Shenzhen University,Shenzhen,518061,China

Lin，Qi,Zhang，Yingfeng,Yang，Shangrui,et al. A self-learning and self-optimizing framework for the fault diagnosis knowledge base in a workshop[J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING,2020,65.

Lin，Qi,Zhang，Yingfeng,Yang，Shangrui,Ma，Shuaiyin,Zhang，Tongda,&Xiao，Qinge.(2020).A self-learning and self-optimizing framework for the fault diagnosis knowledge base in a workshop.ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING,65.

Lin，Qi,et al."A self-learning and self-optimizing framework for the fault diagnosis knowledge base in a workshop".ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 65(2020).