Multivariate Statistical Color Map Visualization
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Solution Overview
Problem
Existing methods for visualizing multivariate statistical outputs from models like PCA and PLS struggle to effectively monitor and analyze process performance over time, making it difficult for human operators to assess statistical anomalies and process incidents efficiently.
Innovation Solution
A method and system that render multivariate statistical output data as a color map on a display, where each time sample is represented as a color patch, with color, height, and width adjusted based on data magnitude, time, and data coverage, allowing for visualization of contributions and anomalies as a function of time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multivariate statistical output data is visualized using traditional methods, then the data can be displayed, but human operators cannot effectively monitor and analyze process performance over time
Solution Approach 1:
The patent transforms multivariate statistical data visualization from traditional scatter plots or time series graphs into a color map representation that adds spatial dimensions. Each color patch's position, color intensity, height, and width encode different statistical parameters, allowing operators to simultaneously perceive magnitude, duration, and distribution of statistical anomalies across multiple dimensions in a single visual field.
Solution Approach 2:
The patent employs color mapping where different colors and color intensities represent different magnitudes of statistical output data. This visual encoding allows human operators to instantly perceive the magnitude and trends of statistical parameters without numerical analysis, transforming abstract multivariate data into intuitive visual patterns that preserve temporal and spatial information.
2Loss of information
If detailed multivariate statistical data is displayed, then comprehensive information is provided, but human operators find it difficult to analyze and understand the outputs
Solution Approach 1:
The patent segments multivariate statistical data into discrete color patches, where each patch represents a specific time sample or group of time samples. This segmentation allows operators to analyze individual statistical events separately while maintaining the ability to perceive overall patterns across the entire dataset, making comprehensive data manageable and analyzable.
Solution Approach 2:
The patent encodes multiple statistical parameters (magnitude, duration, frequency) into spatial and visual properties of color patches. This dimensional transformation converts complex multivariate information into an intuitive visual format where operators can grasp comprehensive process performance through pattern recognition rather than numerical analysis.
3Difficulty of detecting and measuring
If traditional visualization methods are used, then the system complexity is low, but the ability to detect statistical anomalies is insufficient
Solution Approach 1:
The patent uses color intensity and hue variations to encode statistical parameter magnitudes, enabling operators to instantly detect anomalies by identifying unusual color patterns. This visual anomaly detection system automatically highlights deviations from normal process behavior without requiring complex analytical tools or operator training in statistical methods.
Solution Approach 2:
The patent transforms statistical parameters into visual parameters (color, position, size) that are naturally perceptible to human operators. This parameter transformation enhances anomaly detection capability by converting abstract statistical deviations into visually salient features that the human visual system can process instinctively.
Data Source
AI summary
A method, apparatus and module for visualizing multivariate statistical measurements. A processing system receives multivariate statistical output data, such as scores or contributions from a multivariate statistical model and renders the multivariate statistical output data as a function of time as a color map on a display. Each multivariate statistical output data can be obtained at each time sample and rendered as a corresponding color patch on the display. The color, height and width of the corresponding color patch can be adjusted to correspond to the magnitude of the output data, the length of time of the time sample and the number of the output data. Normalized scores/contributions at each time sample can be rendered as a corresponding color patch on the display in response to said Q statistic exceeding said predetermined threshold.