Collaborative Robot Defect Diagnosis Using Severity-Based Data Structuring
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Solution Overview
Problem
Existing collaborative robots in smart factories face challenges in data analysis due to high task complexity, making it difficult to diagnose defects or failures effectively, especially for small and medium-sized enterprises lacking resources and capabilities.
Innovation Solution
A data management method and device that generates, collects, and stores data structures for collaborative robots, including sensing, operation, and failure data, with severity levels defined to facilitate defect diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collaborative robots perform various complex tasks, then versatility and adaptability improve, but data analysis complexity and defect diagnosis difficulty worsen
Solution Approach 1:
The patent segments robot operation data into distinct types (sensing data, operation data, failure data) and organizes them into separate data structures. This segmentation allows complex multi-task robot data to be systematically categorized and analyzed, resolving the contradiction between task versatility and data analysis complexity by providing a structured approach to handle diverse data types.
Solution Approach 2:
The patent introduces a severity level dimension (level 1, level 2, level 3) to categorize operation data, transforming the analysis from a single-dimension approach to a multi-dimensional framework. This additional dimension enables systematic prioritization and analysis of robot defects, making complex data more manageable while maintaining comprehensive task monitoring capability.
2Measurement precision
If all operation data is collected and analyzed, then defect diagnosis accuracy improves, but data management complexity and resource requirements worsen
Solution Approach 1:
The patent extracts and stores only critical failure data in a dedicated failure data structure, separating it from general operation data. By using severity levels to identify and extract only the most important defects (level 2 and level 3), the system achieves high diagnosis accuracy while reducing data management complexity by focusing on essential information rather than processing all collected data equally.
Solution Approach 2:
The patent applies different data management approaches to different types of data based on their importance. Critical failure data receives detailed structured storage and analysis, while other operation data is managed with appropriate but less intensive processing. This local quality differentiation optimizes resource allocation while maintaining high diagnosis accuracy for critical issues.
3Productivity
If severity levels are defined for operation data, then defect prioritization and diagnosis efficiency improve, but data structure complexity worsens
Solution Approach 1:
The patent establishes severity level classifications and data structures in advance, before actual defect diagnosis occurs. By pre-defining level 1, level 2, and level 3 categories and their corresponding data structures, the system enables rapid defect prioritization and efficient diagnosis execution, as the framework is already in place rather than being created during the diagnosis process.
Data Source
AI summary
The present invention relates to a method and device for diagnosing a defect of a collaborative robot, the method comprising the steps in which: an electronic device generates a sensing data structure for managing sensing data collected from at least one collaborative robot; the electronic device generates an operation data structure for managing operation data associated with the operation of the collaborate robot; the electronic device generates a malfunction data structure for managing malfunction data of a point in which the severity of the operation equals to or is higher than a threshold; and the electronic device stores data collected from the at least one collaborative robot in accordance with the structures. Application to other embodiments is also possible.