Inspection Data Visualization for Faster Fault Detection
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Solution Overview
Problem
In manufacturing processes, especially in automobile production, the inspection and condition monitoring of objects such as holders in production systems are costly and time-consuming due to the need for manual detection of deviations and faults, leading to potential downtime.
Innovation Solution
A user-centered approach for automatically assisting with inspection and condition monitoring using a visualization concept that provides a multidimensional representation of sensor data, enabling a comparative analysis of object poses and sensor readings across different categories and situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection of deviations and faults is used in inspection and condition monitoring, then detailed analysis can be performed, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical inspection methods with automated sensor-based detection systems. Sensors automatically capture data about object conditions, eliminating the need for manual measurement while maintaining or improving detection accuracy. This substitution directly reduces inspection time and labor costs.
Solution Approach 2:
The patent introduces sensors as intermediary devices between the object being inspected and the inspector. These sensors act as mediators that automatically detect and transmit condition data, eliminating the need for direct manual examination. This intermediary system enables continuous monitoring without interrupting production processes.
2Reliability
If sensor data is collected for condition monitoring, then real-time information is available, but the data becomes difficult to analyze and interpret
Solution Approach 1:
The patent transforms sensor data from raw numerical values into visual representations displayed on a user interface. By converting data into graphical formats with multiple dimensions (spatial position, temporal trends, severity levels), the system makes complex data intuitively understandable. This dimensional transformation allows maintenance personnel to quickly identify patterns and anomalies without complex analytical skills.
Solution Approach 2:
The patent creates visual copies or representations of the actual object conditions through graphical displays. Instead of presenting raw sensor data, the system generates visual models that replicate the physical state of monitored objects, making it easier to interpret conditions and detect deviations from normal operation.
3Measurement precision
If traditional inspection methods are used, then detailed individual analysis is possible, but overall patterns and trends cannot be easily identified
Solution Approach 1:
The patent merges multiple sensor data streams and inspection results into a unified visual display that shows both individual object conditions and overall patterns simultaneously. The user interface combines detailed individual data with aggregated trend information, allowing maintenance personnel to see both specific fault details and broader system-wide patterns in a single view.
Solution Approach 2:
The patent adds temporal and comparative dimensions to the data presentation by displaying historical trends and comparing multiple objects simultaneously. This multi-dimensional visualization enables pattern recognition across different time periods and objects while maintaining the ability to drill down into individual fault details when needed.
Data Source
AI summary
Provided is a method in which a processor (1) accesses a database which contains a set of data records containing a focus data record, (2) selects first data records from the set of data records, the first context information of which does not correspond to the first context information of the focus data record, and the second context information of which corresponds to the second context information of the focus data record, (3) lines up a focus graphic, (4) selects second data records from the set of data records, the first context information of which corresponds to the first context information of the focus data record, and the second context information of which does not correspond to the second context information of the focus data record, and (5) lines up second graphics.