Production Object Pose Visualization for Faster Deviation Diagnosis
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Solution Overview
Problem
Functional failures and maintenance of holders in production systems, such as those used in automobile manufacturing, result in high costs due to downtime, as existing inspection and condition monitoring methods are inefficient and do not allow for intuitive visualization of deviations.
Innovation Solution
A user-centered approach for automated inspection and condition monitoring using a visualization concept that combines sensor data with a graphical user interface, allowing for the intuitive display of deviations through scaled and target poses, enabling engineers to identify causes of deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional sensor data visualization methods are used (displaying only when limit values are exceeded), then the system maintains simple visualization, but maintenance engineers cannot infer possible causes as the type of deviation is not provided
Solution Approach 1:
The patent transforms one-dimensional sensor data (single value measurements) into three-dimensional visual representations by displaying sensor data points in spatial context relative to target values. This dimensional transformation allows maintenance engineers to perceive not only whether limits are exceeded but also the direction and magnitude of deviations, enabling cause inference without adding complex analytical layers.
Solution Approach 2:
The patent creates visual copies or representations of sensor data in graphical form, where data points are depicted as visual elements in a coordinate system. This visual copying transforms abstract numerical data into intuitive spatial representations that maintain all original information while adding contextual understanding of deviation types, allowing engineers to assess conditions qualitatively without complicating the underlying data collection system.
2Measurement precision
If detailed sensor data analysis is performed to identify deviation types, then maintenance accuracy improves, but the time required for assessment increases
Solution Approach 1:
The patent performs preliminary organization and visualization of sensor data before maintenance assessment is needed. By pre-processing sensor data into intuitive graphical representations that show deviation types and magnitudes, the system prepares information in a ready-to-analyze format. This preliminary action eliminates the need for time-consuming data manipulation during actual maintenance assessments, allowing engineers to quickly interpret deviation characteristics directly from the visual display.
3Ease of operation
If sensor data is presented in tabular format with limit value comparisons, then the visualization remains simple, but maintenance engineers experience difficulty in qualitative assessment
Solution Approach 1:
The patent transforms one-dimensional tabular data into two-dimensional or three-dimensional visual displays where sensor values are represented as points in space relative to target values. This dimensional transformation converts abstract numerical comparisons into intuitive spatial relationships, allowing maintenance engineers to immediately perceive deviation direction, magnitude, and type through visual position rather than numerical analysis, thereby enhancing qualitative assessment capability.
Data Source
AI summary
A method for the automated support of an inspection and/or condition monitoring of objects of a production system is provided. A processor takes or calculates an actual pose of an object. The actual pose specifies a translation and/or rotation with respect to a target pose of the object. A processor calculates a scaled pose of the object from the actual pose of the object by scaling the translation and/or rotation with respect to the target pose of the object. The processor displays an animated focus graphic. The focus graphic displays alternately a graphical image of the object in the target pose and a graphical image of the object in the scaled pose, and the scaling is selected such that a deviation of the actual pose from the target pose, which deviation is diagnostically relevant for the inspection and/or condition monitoring of the object, is clearly perceptible.