Pipeline Risk Modeling With Multi-Source Failure Prediction
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Solution Overview
Problem
Current pipeline risk models face challenges in accurately identifying and managing uncertainties and ambiguities in pipeline failure risks due to insufficient or uncertain data, leading to inefficiencies in risk assessment and management.
Innovation Solution
A computational risk modeling system that integrates data from in-line inspection vehicles, external companion devices, and geo-positioning systems to generate predictive models of failure modes, using a Cluster Machine with transition functions and machine learning to improve data processing and risk monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk models are used for pipeline assessment, then the system is simpler to operate, but the measurement precision and reliability of risk assessment deteriorates due to insufficient or uncertain data
Solution Approach 1:
The risk assessment model is segmented into multiple specialized components including corrosion risk modules, mechanical integrity modules, and external damage modules. Each segment processes specific types of data and uncertainty, allowing the overall system to achieve high measurement precision through specialized sub-systems rather than a single complex monolithic model.
Solution Approach 2:
The patent introduces probabilistic dimensions to traditional deterministic risk models by incorporating probability density functions, confidence intervals, and uncertainty propagation through Bayesian networks. This adds a dimensional layer of statistical rigor that transforms qualitative risk assessments into quantitative measurements with defined precision levels.
2Reliability
If comprehensive data collection from multiple sources is implemented, then the reliability of risk assessment improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent merges data from diverse sources including inline inspection tools, external monitoring systems, historical failure data, and environmental databases into a unified probabilistic risk assessment framework. This consolidation through standardized data interfaces and integrated databases improves reliability by comprehensively capturing all relevant risk factors while managing complexity through systematic integration rather than separate isolated systems.
Solution Approach 2:
Probabilistic models and Bayesian networks serve as intermediary layers between raw data from multiple sources and final risk assessment outputs. These intermediaries process, validate, and harmonize data from different sources, transforming heterogeneous inputs into standardized probability distributions that can be reliably combined, thereby improving overall system reliability while abstracting away the complexity of data integration.
3Measurement precision
If advanced computational models with machine learning are used, then the detection precision of failure modes improves, but the loss of time for data processing and model computation increases
Solution Approach 1:
The system performs preliminary actions by pre-computing probability distributions, failure mode signatures, and risk thresholds during system initialization or offline periods. Machine learning models are pre-trained on historical data to establish baseline patterns of failure modes. This preliminary processing reduces real-time computation requirements, allowing the system to achieve high detection precision by comparing current data against pre-established patterns rather than performing full computational analysis during time-critical operations.
Solution Approach 2:
The patent implements partial computation strategies where the full probabilistic model is not always executed. Instead, the system performs selective analysis based on risk thresholds and data quality indicators, applying comprehensive computational models only when uncertainty exceeds predefined levels or when critical failure modes are suspected. This partial action approach maintains high detection precision for critical cases while reducing overall computation time by skipping unnecessary full-model executions for low-risk scenarios.
Data Source
AI summary
A system for operation and integrity management of a pipeline stores field data obtained from an operational system, and in-line data obtained from an in-line inspection vehicle and external data from a video camera on an external companion device. The system performs data processing on the in-line data and field data to generate input for risk modeling and performs risk modeling of the pipeline using the input to predict a risk of one of a plurality of failure mode states at a portion of the pipeline. The system may initiate risk monitoring for the portion of the pipeline by the operational system and the in-line inspection vehicle.