Pipe Network Failure Classification Using Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current system monitoring technologies for pipe networks lack effective methods for real-time mechanical failure classification, condition assessment, and remediation recommendation, leading to potential leaks and economic losses, as they require expensive inline inspections and do not account for operational and environmental factors.
Innovation Solution
A system comprising sensors and a processor that monitor pipe operations, analyze data, and classify failures based on operational and environmental conditions, using predictive statistical analysis to determine potential future failures, thereby recommending remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring systems are used to provide extensive feedback about pipe network status, then reliability and detection capability are improved, but device complexity and cost increase
Solution Approach 1:
The monitoring system is segmented into multiple functional modules: sensor layer for data collection, communication layer for data transmission, and processing layer for analysis. This modular segmentation reduces overall system complexity while maintaining comprehensive monitoring capabilities across the pipe network.
Solution Approach 2:
The monitoring system is designed with multi-functionality to handle various pipe conditions (leaks, pressure changes, flow anomalies) using a single integrated platform. This universal approach avoids the need for separate specialized systems for each monitoring task, reducing device complexity while improving reliability.
2Measurement precision
If inline inspections are conducted to assess pipe condition, then measurement precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The system performs preliminary continuous monitoring and data collection during normal pipe operation, assessing pipe condition proactively before failures occur. This eliminates the need for time-consuming scheduled inline inspections while maintaining high measurement precision through ongoing sensor data analysis.
Solution Approach 2:
Physical inline inspection mechanisms are replaced with sensor-based monitoring systems that continuously measure pipe conditions remotely. This substitution eliminates the need to stop pipe operations for mechanical inspections, reducing time loss while maintaining measurement precision through electronic sensing.
3Measurement precision
If comprehensive sensor deployment is implemented for continuous monitoring, then detection capability is improved, but loss of energy and operational cost increase
Solution Approach 1:
The monitoring system uses periodic sampling of sensor data at optimized intervals rather than continuous high-frequency monitoring. This periodic action maintains adequate detection precision for identifying pipe failures while significantly reducing energy consumption compared to truly continuous monitoring.
Solution Approach 2:
The system deploys sensors strategically at critical locations where failures are most likely to occur or have greatest impact, rather than uniform comprehensive coverage. This partial monitoring approach achieves effective detection precision with reduced sensor count and lower energy consumption.
Data Source
AI summary
A system for pipe network failure classification, may include one or more sensors, a pipe network parts database, and at least one processor communicatively networked to the one or more sensors and the pipe network parts database. The at least one processor is configured to intermittently receive sensor collected parameters from the one or more sensors, reference, upon receipt of an indication of a failure of a pipe in the pipe network, records of the pipe network parts database and retrieve one or more feature parameter values associated with the failed pipe's operational or environmental conditions, and classify the pipe failure into one of two or more failure categories associated with different failure causes, based on values of parameters associated with the failed pipe that are received from at least one of the one or more sensors or the pipe network parts database.