Asset Failure Risk Forecasting with Reusable Hazard Model Groups
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Solution Overview
Problem
The processing of large volumes of data related to pipeline infrastructure is time-consuming and computationally intensive, requiring advanced data processing techniques to optimize operational efficiency and identify potential risks without compromising data quality.
Innovation Solution
A method and system utilizing a modified Weibull Proportional Hazard Model to group data attributes and calculate failure rates by reusing previously computed values, reducing the need for repetitive calculations and optimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data processing methods are used to calculate failure rates for pipeline assets, then comprehensive risk assessment can be achieved, but processing time and computational resources are excessive
Solution Approach 1:
The patent segments the hazard model into multiple sub-portions and groups assets with identical attribute combinations together. By dividing the computation into manageable segments and processing groups rather than individual assets, the system reduces redundant calculations while maintaining comprehensive risk assessment coverage.
Solution Approach 2:
The system performs preliminary grouping of assets by their attribute combinations before executing the hazard model calculations. By pre-organizing assets into groups with identical attributes, the system prepares the data structure in advance to enable efficient reuse of computation results across multiple assets, significantly reducing processing time.
2Measurement precision
If traditional data processing methods are used to evaluate hazard models for multiple assets, then accurate failure rates can be obtained, but computational resources are excessively consumed
Solution Approach 1:
The patent merges assets with identical attribute combinations into single computation groups. By combining multiple assets that share the same hazard model parameters into one evaluation unit, the system calculates failure rates once per group rather than separately for each asset, dramatically reducing computational resource consumption while preserving measurement precision.
Solution Approach 2:
Instead of recalculating hazard model results for each asset with identical attributes, the system creates and stores reusable computation results that can be copied and applied to all assets within a group. This copying approach eliminates redundant computational work while maintaining the accuracy of failure rate measurements.
3Reliability
If comprehensive data from multiple attributes is processed for each asset individually, then complete risk assessment is achieved, but processing complexity increases
Solution Approach 1:
The patent segments the complex multi-attribute processing task into smaller sub-tasks by first grouping assets with identical attribute combinations. This segmentation transforms a single complex processing operation into multiple simpler operations: grouping, representative calculation, and result distribution, thereby reducing processing complexity while maintaining assessment completeness.
Solution Approach 2:
The system creates a universal computation framework where a single hazard model evaluation can serve multiple assets simultaneously. By designing the system to process groups of assets with identical attributes through a unified approach, the patent reduces processing complexity while ensuring complete risk assessment coverage across all assets.
Data Source
AI summary
A system and method for forecasting failure in a plurality of assets wherein the system comprises one or more computing devices connected to a server through a network, wherein the server comprises a memory that stores reusable group attribute values, and one or more processors coupled to the memory. The one or more processors comprise program instructions that when executed, cause the one or more processors to forecast one or more failure rates for said assets based on a hazard model. The method comprises identifying a subgroup of assets and attributes of a specific plurality of assets and attributes corresponding to a grouped attribute combination, retrieving reusable grouped attribute values from the memory, and replacing a process of evaluating the subgroup of assets and attributes according to the hazard model, with the retrieved reusable grouped attribute values.