Bayesian Nonparametric Infrastructure Failure Prediction
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Solution Overview
Problem
Current methods for predicting failures in critical infrastructure, such as water supply systems and bridges, are inefficient as they rely on manual inspections and do not accurately account for varying component conditions, leading to potential service interruptions and high maintenance costs.
Innovation Solution
A computer-implemented method using a Bayesian nonparametric statistical model with a beta process to estimate the failure likelihood of infrastructure components, allowing for flexible and accurate predictions based on historical data, including grouping components by properties and utilizing a hierarchical beta process to account for dependencies and sparsity in data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to predict infrastructure failures, then operational simplicity is maintained, but prediction accuracy and reliability deteriorate
Solution Approach 1:
The patent transforms the prediction approach by changing from fixed parametric models to nonparametric Bayesian models that adapt parameters dynamically. The beta process allows the model to learn failure rates from historical data without assuming a fixed distribution, enabling accurate predictions while maintaining computational tractability through hierarchical structure.
Solution Approach 2:
The patent replaces manual inspection mechanisms with an automated statistical modeling system. The Bayesian nonparametric model automatically processes historical failure data and generates predictions, substituting human judgment with a systematic mathematical framework that provides consistent and scalable predictions across infrastructure components.
2Measurement precision
If parametric statistical models are used for failure prediction, then model simplicity is maintained, but prediction accuracy deteriorates due to structural assumptions
Solution Approach 1:
The patent introduces dynamics into the statistical model by using the beta process, which allows the failure rate parameters to evolve over time based on observed data. Unlike static parametric models, this dynamic approach adapts to changing infrastructure conditions and failure patterns, providing accurate predictions for components with varying failure behaviors.
Solution Approach 2:
The patent segments the infrastructure into individual components, each with its own failure likelihood estimate derived from the hierarchical beta process. This segmentation allows the model to capture component-specific failure patterns while sharing information across the infrastructure through the hierarchical structure, improving accuracy for each individual component.
3Measurement precision
If aggregate infrastructure-level failure estimates are used, then computational efficiency is maintained, but prediction specificity deteriorates
Solution Approach 1:
The patent divides the infrastructure into discrete components and applies the beta process at the component level through hierarchical modeling. This segmentation enables specific failure likelihood estimates for each component while leveraging shared data across the infrastructure, achieving both precision and computational efficiency through the hierarchical structure.
Solution Approach 2:
The patent merges individual component models into a hierarchical framework that shares information across components. The hierarchical beta process combines component-specific failure data with infrastructure-wide patterns, allowing efficient computation by pooling information while maintaining component-specific predictions.
4Reliability
If historical failure data is sparsely distributed, then data collection simplicity is maintained, but prediction reliability deteriorates
Solution Approach 1:
The patent merges data from multiple sources and time periods within the hierarchical beta process framework. By combining component-specific failure data with infrastructure-wide patterns and borrowing strength across similar components, the model achieves reliable predictions even when individual component data is sparse.
Solution Approach 2:
The Bayesian framework incorporates feedback from observed failures to continuously update failure likelihood estimates. As new failure data becomes available, the model learns and adapts, improving prediction reliability over time while efficiently utilizing even small amounts of historical data through the probabilistic updating mechanism.
Data Source
AI summary
The present invention generally relates to failure prediction of an infrastructure (110). A failure likelihood for one or more components of an infrastructure (110) is determined. History data (210) representing prior failures of the components of the infrastructure (110) is applied (230-250) to a Bayesian nonparametric statistical model using a beta process. Then the failure likelihood of one or more components of the infrastructure from the Bayesian nonparametric statistical model is estimated (270). Aspects of the invention include computer-implemented methods (200, 300), software and computer systems (100).