A self-learning and self-optimizing framework for the fault diagnosis knowledge base in a workshop. (October 2020)

• Record Type: Journal Article
• Authors: Lin, Qi; Zhang, Yingfeng; Yang, Shangrui; Ma, Shuaiyin; Zhang, Tongda; Xiao, Qinge
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 65(2020)
• Publication Date: 2020-10
• Subjects: Knowledge base -- Semi-supervised multi-spatial manifold clustering -- Generative adversarial network -- Self-optimizing -- Data-driven -- Knowledge-driven; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2020.101975
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: A self-learning and self-optimizing framework is proposed for the knowledge base. A dynamic clustering model is used to adapt to the diversity of fault. A knowledge evolution model is designed to optimize the knowledge base. The efficiency and effectiveness are illustrated by a case study. Abstract: 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.