Research and development of a closed-loop inspection system for cartesian robots based on a self-diagnosis model
[Objective]With the steady advancement of industrial automation,Cartesian robots have become increasingly prevalent in intelligent manufacturing.However,traditional inspection systems for these robots struggle with issues such as incomplete detection parameters and a lack of self-diagnosis capabilities,which are insufficient to meet emerging market demands.This study aims to address these challenges by developing a closed-loop inspection system that incorporates a self-diagnosis model for Cartesian robots.The proposed system seeks to enhance the inspection efficiency and accuracy of Cartesian robots,ensuring their performance and safety and promoting the healthy development of the intelligent manufacturing industry.[Methods]This paper first identifies the core technologies and inspection objectives necessary for a Cartesian robot inspection system,comparing existing inspection standards with new market demands.Next,a knowledge-based self-diagnosis model is proposed and established,integrating fault tree analysis,function lists,and mathematical models to form the foundation of the knowledge base management system.The system employs rule-based reasoning,functional verification,and real-time calculation techniques within the inference engine to facilitate decision-making regarding detection results.To implement the system,a modular programming approach is adopted,segmenting it into application and interface layers for design simplicity and flexibility.The application layer revolves around the four primary modules of the Cartesian robot system,while the interface layer encapsulates interface classes for the control card,enabling cross-platform migration through a link library,thereby enhancing system scalability.In building the knowledge base,the objectives and scope are first outlined,followed by the collection and organization of relevant knowledge.Appropriate methods are then utilized to represent and shape the initial knowledge base.For constructing the fault detection knowledge base,this paper employs fault tree modeling and analysis techniques.This includes conducting quantitative assessments of the probability importance and critical importance of key events and representing knowledge through production rules.[Results]The implementation of this system reveals several key outcomes:① The closed-loop inspection system for Cartesian robots,rooted in the self-diagnosis model,can encompass vital modules such as the control system and execution mechanism,ensuring comprehensive detection of performance,functions,and faults.② By incorporating a knowledge-based management system and an intelligent reasoning mechanism,the inspection system possesses self-learning and self-iteration capabilities,thus continuously enhancing inspection efficiency and accuracy.③ The system demonstrates scalability and compatibility,adapting seamlessly to the inspection requirements of diverse types and specifications of Cartesian robots.[Conclusions]The intelligent inspection system devised for Cartesian robots in this paper addresses prevalent issues in traditional industrial robot inspection systems and aligns with the emerging market demands for Cartesian robot inspection.This system employs a self-diagnosis model anchored in a knowledge base,combining fault tree analysis,rule-based reasoning,and other techniques for comprehensive detection.Experimental validation has shown that the system performs exceptionally well in fault detection,functional verification,and performance evaluation.Future efforts will focus on refining and enhancing this system to better cater to the application needs of Cartesian robots in intelligent manufacturing and industrial automation.
industrial robotclosed-loopdetection systemself-diagnosis modelknowledge base
万方数据
维普
