Knowledge representation and decoupling analysis on failure mechanisms of remotely controlled intelligent machinery

Abstract

Remotely controlled intelligent machinery has complications, including loose management of failure information, low information availability, and coupling influence among systems, which can be effectively solved by analyzing the system state and information characteristics of the equipment. Taking intelligent agricultural machinery as the object, this study applies the knowledge representation method to explore equipment failure states' informational features and construct a knowledge framework model of system failure representation relations and a complex network conceptual model to visualize the failure information more intuitively and facilitate systematic management and utilization. The feedback-based decoupling analysis method uncouples the coupling between subsystems, identifying the critical state of decoupling well. It attempts to apply the knowledge representation and decoupling analysis to remotely controlled intelligent agricultural machinery equipment. Through the example, the result further illustrates the feasibility of knowledge representation and decoupling for remotely controlled intelligent agricultural machinery systems and provides essential support for better failure analysis.