Manufacturing Data Visualization for Prioritized Abnormality Detection
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Solution Overview
Problem
In manufacturing industries, as the number of items to be inspected for abnormalities increases, the probability of overlooking abnormalities also rises, making it difficult to detect issues at an early stage effectively.
Innovation Solution
A visualized data generation device and system that includes an acquisitor, analyzer, and generator to acquire, analyze, and display manufacturing data, deriving index values and generating visualized data with prioritized information display regions based on these values to highlight abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of inspection items is increased to improve abnormality detection coverage, then the detection capability is improved, but the probability of overlooking abnormalities increases and the complexity of data management worsens
Solution Approach 1:
The patent segments the complex manufacturing data into multiple categories (normal data, abnormal data, reference data) and organizes them in a hierarchical database structure. This segmentation allows the system to manage numerous inspection items systematically without increasing overall system complexity, as each category can be handled independently with specific processing rules.
Solution Approach 2:
The patent introduces an abnormality determination unit as an intermediary component that mediates between the acquired manufacturing data and the final abnormality detection result. This unit applies reference data and predetermined rules to filter and interpret the large volume of inspection data, preventing information overload and reducing the complexity of direct data-to-conclusion processing.
2Reliability
If multiple inspection items are monitored simultaneously to improve comprehensive detection, then the detection coverage is improved, but the ease of operation deteriorates due to information overload
Solution Approach 1:
The patent applies local quality by providing different types of visualized information for different data categories. Normal data is displayed with basic visual indicators, while abnormal data receives special highlighting and detailed analysis displays. This differentiated presentation allows operators to easily identify critical information without being overwhelmed by uniform detailed displays of all data.
Solution Approach 2:
The patent utilizes color changes as a visual differentiation mechanism in the display unit. Different colors represent different data states (normal, abnormal, reference), enabling operators to quickly scan and identify abnormalities among multiple inspection items without reading detailed numerical data for each item, thus maintaining ease of operation with comprehensive monitoring.
3Loss of information
If detailed information is displayed for all inspection items to improve information completeness, then the information completeness is improved, but the loss of time increases due to difficulty in identifying critical abnormalities
Solution Approach 1:
The patent performs preliminary action by pre-processing manufacturing data through the abnormality determination unit before display. Reference data is established in advance, and the system automatically compares acquired data against these references, pre-identifying potential abnormalities. This preliminary processing ensures that when data is displayed, critical information is already highlighted, maintaining completeness while reducing identification time.
Solution Approach 2:
The patent extracts and separates abnormal data from normal data in the visualized information display. Instead of presenting all inspection item details uniformly, the system extracts only the abnormal items and presents them with special visual markers. This extraction maintains information completeness for all items while allowing operators to quickly locate critical abnormalities without scanning through all detailed information.
Data Source
AI summary
A visualized data generation device includes an acquisitor, an analyzer, and a generator. The acquisitor acquires manufacturing data including one or more pieces of first data Yi regarding a product state with respect to one product. The analyzer analyzes the first data Yi acquired by the acquisitor and derives a first index value with respect to each piece of the first data Yi. The generator generates visualized data including a first analysis result display region for causing a display device to display information about the first index value. The generator generates the visualized data in which an amount of information and a priority is set for each first analysis result display region on the basis of the first index value and a display form of the first analysis result display region is set on the basis of the amount of information and the priority.