Lightning Damage Prediction for Access Equipment
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Solution Overview
Problem
Existing methods for predicting lightning damage failures in access equipment face challenges due to the difficulty in collecting detailed data for various components, especially when recovery operations prioritize functionality over data collection, leading to insufficient data for accurate predictions.
Innovation Solution
A lightning damage prediction apparatus that utilizes regression equations generated from historical lightning strike and equipment density data to predict failure densities across sections, allowing for risk ranking and mapping of high-risk areas, even when detailed failure data is unknown.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed failure data collection is performed for each component, then prediction accuracy is improved, but recovery operation efficiency deteriorates due to time-consuming data collection
Solution Approach 1:
The prediction target area is divided into multiple sections, and failure density is calculated for each section separately. This segmentation allows the system to work with aggregated section-level data rather than requiring detailed component-level data collection during recovery operations, thus maintaining prediction accuracy while reducing data collection time
Solution Approach 2:
The system calculates failure density for each section and identifies only the sections with high failure density as prediction targets. This partial action approach focuses computational resources on high-risk areas only, improving prediction efficiency without requiring comprehensive data collection across all areas
2Reliability
If comprehensive equipment data is collected for all components, then prediction reliability is improved, but system complexity increases due to data management requirements
Solution Approach 1:
The system merges equipment data and failure data at the section level rather than maintaining separate detailed records for each component. By aggregating data to the section level and calculating failure density as a combined metric, the system achieves reliable predictions while significantly reducing data management complexity
Solution Approach 2:
The failure density calculation method serves multiple functions: it predicts lightning damage risk, identifies high-risk sections, and guides preventive maintenance priorities. This universal approach eliminates the need for separate data management systems for different prediction purposes, reducing overall system complexity
3Ease of operation
If section-level aggregation is used instead of component-level data, then data collection ease is improved, but measurement precision deteriorates due to loss of detailed information
Solution Approach 1:
The system applies different levels of detail to different sections based on their failure density characteristics. High-risk sections receive more focused analysis and prediction resources, while low-risk sections use aggregated data. This local quality approach maintains sufficient precision for prediction purposes while easing data collection requirements
Data Source
AI summary
A lightning damage prediction apparatus that predicts a risk of a failure of access equipment due to a lightning strike is provided. A regression equation representing a relationship between a lightning strike density for each section in a prediction target area, an equipment density for each section of components of a plurality of types constituting equipment, and a lightning damage failure density for each section of the equipment or the component, corresponding to a first period in the past, is generated, and a lightning damage failure density for each section of a prediction target area corresponding to a second period is predicted on the basis of a lightning strike density and an equipment density for each section in the prediction target area corresponding to the second period that is a prediction target, and the regression equation.