Dynamic Gravitational Well Display for Proactive System Failure Prediction
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Solution Overview
Problem
Current methods for analyzing errors in complex systems, such as healthcare and aviation, are inadequate as they do not account for human factors and provide real-time, proactive insights into system performance, leading to potential catastrophic events.
Innovation Solution
A Totally Integrated Intelligent Dynamic Systems Display that graphically represents a complex system's status using a gravitational well model with concentric ridges and wedge-shaped regions, incorporating sensors and a graphics generator to provide real-time data on subsystem functioning levels and trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error analysis models (chain of events, swiss cheese) are used, then error analysis can be performed, but real-time proactive insight is not provided and human factors are not accounted for
Solution Approach 1:
The patent implements a dynamic error model that continuously updates system state representations in real-time, transitioning from static error analysis models to a living system that adapts to changing conditions. The model dynamically adjusts error probabilities and system state assessments based on incoming data, enabling proactive identification of failure risks before they materialize.
Solution Approach 2:
The system incorporates continuous feedback loops where sensor data about actual system performance is compared against predicted outcomes. This feedback mechanism allows the error model to learn from real-time observations and adjust its assessments, providing ongoing proactive insights into system reliability while accounting for human factors and environmental variations.
2Measurement precision
If detailed real-time monitoring of all subsystems is implemented, then accurate system status information is obtained, but system complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex system into manageable subsystems, each monitored independently with its own error model. This segmentation allows detailed monitoring of critical components without requiring comprehensive monitoring of every element, reducing overall system complexity while maintaining measurement precision for the most important subsystems.
Solution Approach 2:
The error model applies different levels of monitoring and analysis to different subsystems based on their criticality and error probabilities. High-risk subsystems receive detailed real-time monitoring, while lower-risk components are monitored at coarser intervals, optimizing the balance between measurement precision and system complexity.
3Loss of time
If reactive error analysis is used, then historical errors can be examined, but proactive prevention of catastrophic events is not achieved
Solution Approach 1:
The patent implements preliminary action by continuously predicting potential failures before they occur. The error model proactively identifies subsystems at risk of failure and alerts operators in advance, enabling preventive action to be taken before catastrophic events materialize. This transforms the temporal focus from post-event analysis to pre-event prediction.
Solution Approach 2:
The system dynamically adjusts its prediction horizons and alert thresholds based on real-time system state and error probabilities. This dynamic approach allows the system to provide timely proactive warnings when conditions indicate impending failure, while adapting to changing system characteristics to maintain optimal response timing.
Data Source
AI summary
An apparatus for graphically displaying analytical data, comprising a generic model to graphically represent a complex set of physical characteristics potentially leading to catastrophic failure of a physical system. A central region represents failure of the physical system; a series of concentric ridges represents level of function. Wedge shaped regions upon the ridges represent predetermined subsystems of the physical system. An icon is displayed with changes in the icon proportional to time, and any one or more of color, position, shape, and rotation of the icon representing prescribed analysis of the physical characteristics. Mappings correlate predetermined characteristics of the system with failure of the physical system, levels of functioning represented by the ridges, subsystems represented by the wedge shaped regions, and/or analysis of the physical characteristics represented in the icon. A sensor interface receives measurements, and a graphics generator prepares a presentation of the model.