IoT Gas Pipeline Corrosion Mapping for Targeted Inspection
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Solution Overview
Problem
Gas pipelines are prone to corrosion due to underground environmental factors, leading to potential leaks, explosions, and safety hazards, necessitating effective corrosion supervision and repair methods.
Innovation Solution
A smart gas safety management platform utilizing an IoT system constructs a pipeline diagram using inspection data, determines corrosion probability through machine learning, identifies estimated corrosion regions, and generates a repair plan based on in-depth inspection data and monitoring data to address pipeline corrosion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inspection methods are used for gas pipelines, then the inspection process is simple, but the detection precision and ability to identify corrosion regions is insufficient
Solution Approach 1:
The pipeline is divided into multiple inspection segments with monitoring devices installed at specific intervals. The pipeline diagram is segmented into nodes (monitoring device locations) and edges (pipeline sections), allowing localized corrosion detection without inspecting the entire pipeline uniformly. This segmentation enables precise identification of corrosion regions while reducing overall system complexity.
Solution Approach 2:
The system performs preliminary corrosion probability assessment using the machine learning model before conducting detailed in-depth inspections. By first identifying high-risk regions through the corrosion probability model, the system prepares targeted inspection plans, avoiding comprehensive expensive inspections while maintaining high detection precision.
2Measurement precision
If comprehensive inspection of the entire pipeline is performed, then the detection precision is high, but the loss of time and inspection cost increases
Solution Approach 1:
Instead of inspecting the entire pipeline with equal detail, the system applies partial action by performing in-depth inspections only on identified high-risk corrosion regions. The machine learning model predicts corrosion probability for different segments, and only those exceeding threshold values undergo detailed inspection, significantly reducing time while maintaining high accuracy in identifying actual corrosion problems.
Solution Approach 2:
The system uses automatically collected monitoring data from sensors distributed along the pipeline to feed the machine learning model, which autonomously generates corrosion probability assessments and identifies risk regions. This self-service capability eliminates the need for manual comprehensive inspections, reducing inspection time while maintaining or improving detection accuracy through continuous data-driven analysis.
3Measurement precision
If monitoring devices are installed at all pipeline locations, then the measurement precision is maximized, but the device complexity and cost increase
Solution Approach 1:
Monitoring devices are strategically installed at specific locations (nodes) rather than uniformly distributed throughout the pipeline. The system assigns different qualities/functions to different nodes based on their importance and corrosion risk. High-risk areas have denser monitoring, while low-risk areas have sparser monitoring, optimizing the balance between measurement precision and system complexity.
Solution Approach 2:
The machine learning model serves multiple functions: it processes data from monitoring devices, predicts corrosion probability, identifies high-risk regions, and generates inspection recommendations. This multi-functional approach allows the system to achieve high measurement precision using a relatively simple monitoring infrastructure, as the intelligent model extracts maximum value from limited sensor data.
4Measurement precision
If detailed in-depth inspection is performed on all pipeline sections, then the corrosion feature detection is accurate, but the productivity and efficiency decrease
Solution Approach 1:
The system performs detailed in-depth inspections only on pipeline sections identified as high-risk by the machine learning model. Low-risk sections receive minimal or no in-depth inspection. This partial action approach maintains high corrosion feature detection accuracy for problematic areas while dramatically improving overall inspection efficiency by avoiding unnecessary detailed examinations of healthy pipeline sections.
Data Source
AI summary
A method and an Internet of Things system for corrosion supervision of a gas pipeline are provided. The method includes: obtaining inspection data of a gas pipeline in a gas pipeline network; constructing a first pipeline diagram of the gas pipeline network based on the inspection data; determining a corrosion probability of the gas pipeline based on the first pipeline diagram using a corrosion probability model; determining one or more estimated pipeline corrosion regions based on the corrosion probability of the gas pipeline; obtaining in-depth inspection data of the gas pipeline by performing in-depth inspection on at least one of the estimated pipeline corrosion regions; determining corrosion features of the one or more estimated pipeline corrosion regions based on gas monitoring data and the in-depth inspection data of the gas pipeline; and determining a repair plan based on the one or more estimated pipeline corrosion regions and/or the corrosion features.