Sensor Diagnosis Visualization for Multi-Cycle Status Comparison
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Solution Overview
Problem
Current diagnosis methods for equipment status comparison, such as vibration levels before and after maintenance, struggle to easily identify significant changes and relative relationships between data points, especially in equipment with multiple processes in one operation cycle.
Innovation Solution
A diagnosis device and method that calculates normalized values and group data based on mean and standard deviation, allowing for visual comparison of time series data and relative relationships across operation cycles, using a processor to output diagrams and tables for easy analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual inspection of sensor indicated values is performed, then simplicity of operation is maintained, but ability to identify significant changes and relative relationships deteriorates
Solution Approach 1:
The patent transforms raw sensor data into normalized values by changing the parameter representation. It calculates normalized values using mean and standard deviation, converting absolute sensor readings into relative positions (e.g., 1st to 10th rank) that highlight significant changes and relationships across different sensors and time periods, making patterns visible without complex analysis.
2Measurement precision
If detailed data analysis is performed, then measurement precision of changes is improved, but ease of operation deteriorates
Solution Approach 1:
The patent applies parameter transformation by converting detailed sensor data into ranked normalized values. This allows precise identification of changes while maintaining ease of operation through visual comparison of ranks and groups, avoiding the need for complex statistical analysis by users.
Solution Approach 2:
The patent uses visual representation with color-coded diagrams to display normalized values and groupings. Different colors indicate different groups of sensors based on their normalized values, enabling precise change detection through visual patterns rather than numerical analysis, thus maintaining ease of operation.
3Measurement precision
If multiple sensors are monitored individually, then measurement precision is maintained, but device complexity increases
Solution Approach 1:
The patent merges data from multiple sensors by normalizing them to a common scale based on mean and standard deviation. Sensors are grouped into categories (e.g., 1st to 10th rank groups) that reflect their relative states, allowing simultaneous monitoring of multiple sensors without increasing operational complexity, as all sensors can be compared using the same normalized framework.
Data Source
AI summary
The diagnosis device executes: processing of determining a mean value and a standard deviation of reference data which is data in a reference period in the time series data and then calculating a normalized value of comparison data which is data in a comparison target period in the time series data from the mean value and the standard deviation; processing of executing processing of grouping pieces of data of the plurality of sensors in order of a magnitude relationship between the pieces of data for each of the reference data and the comparison data; and processing of outputting a screen having a diagram which visually shows the normalized value of the comparison data and a table which represents a correspondence between ranking of a group of the magnitude relationship between the pieces of data and each of the plurality of sensors for each of the reference data and the comparison data.