Hazard Function Modeling for Power Asset Failure Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for predicting asset degradation in electric power systems lack objectivity and rely heavily on assumptions or human expert knowledge, complicating maintenance planning and increasing the risk of unexpected failures.
Innovation Solution
A method and processing system that estimate a hazard function for asset failure using outage data collected during field operations, incorporating maintenance data to determine hazard function parameters in a data-driven manner, thereby improving the objectivity of asset state prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive techniques are used for asset degradation monitoring, then expert knowledge and assumptions can guide maintenance planning, but the objectivity and accuracy of failure risk prediction deteriorates
Solution Approach 1:
The patent replaces expert-based subjective judgment with an automated statistical modeling system that processes outage data to estimate hazard functions. The processing system automatically fits probability distributions to historical data, eliminating the need for manual expert assessment while improving objectivity and accuracy of failure risk predictions.
Solution Approach 2:
The system enables self-service by automatically generating maintenance recommendations based on data-driven hazard function estimates. The processing system independently analyzes outage data, determines appropriate probability distributions, and produces actionable insights without requiring continuous expert intervention, thereby reducing maintenance planning complexity.
2Reliability
If more comprehensive outage data is collected and processed, then the objectivity of hazard function estimation improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent transforms complex outage data into standardized hazard function parameters by fitting probability distributions. The processing system changes the parameter representation from raw outage records to condensed statistical parameters (mean, standard deviation, shape parameters) that capture essential failure characteristics while reducing data complexity for downstream analysis.
Solution Approach 2:
The system extracts essential failure risk characteristics from comprehensive outage data by estimating hazard functions and their parameters. It separates the critical information (failure rates, distribution shapes) from the raw data complexity, retaining only the necessary parameters needed for reliable prediction while discarding redundant details.
3Measurement precision
If hazard function parameters are determined without considering maintenance data, then the estimation process is simpler, but the accuracy of asset state prediction in presence of maintenance deteriorates
Solution Approach 1:
The patent implements a dynamic parameter determination approach that adapts hazard function estimation to account for maintenance effects. The processing system adjusts hazard parameters based on maintenance history and timing, allowing the model to dynamically reflect the reduced failure risk following maintenance activities while maintaining accurate predictions throughout the asset lifecycle.
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
To perform an asset state prediction for an electric power system (11, 20), a processing system (30) is operative to estimate a hazard function that is indicative of a probability of asset failure as a function of a variable in absence of asset maintenance. The processing system (30) is operative to determine a hazard function parameter value for at least one hazard function parameter of the hazard function based on outage data for the electric power system (11, 20).