Rising Main Pipeline Pressure Monitoring for Fault Assessment
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Solution Overview
Problem
Rising main pipelines lack advanced instrumentation and monitoring technologies, leading to increased risk of failure and difficulty in managing aging assets, with existing methods providing limited insights into pipeline conditions and often requiring specialized expertise.
Innovation Solution
A system that utilizes hydraulic signals generated during pipeline operation as raw data to provide condition assessment, using data processing techniques and visualization tools to raise automated and engineer-driven alarms for abnormal operations, and to identify issues like leaks, air pockets, and valve malfunctions, without the need for external devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced instrumentation and monitoring technologies are installed on rising main pipelines, then measurement precision and reliability of pipeline condition assessment are improved, but device complexity and cost increase
Solution Approach 1:
The system uses the pipeline's own operational hydraulic signals (pressure, flow variations during normal pumping) as the monitoring data source. The pipeline essentially monitors itself using its own operational characteristics, eliminating the need for external pulse-generating devices or complex instrumentation while maintaining assessment capability
Solution Approach 2:
The invention extracts useful monitoring information from the existing operational hydraulic signals that are already present during normal pipeline operation. By analyzing pressure and flow variations during regular pumping cycles, the system extracts condition assessment data without adding external monitoring devices
2Loss of information
If external devices are used to generate hydraulic events for pipeline monitoring, then measurement capability is improved, but device complexity increases
Solution Approach 1:
The pipeline system generates its own hydraulic signals through normal operational variations in pumping. The system captures and analyzes these self-generated signals to assess pipeline conditions, eliminating the need for external pulse-generating devices or complex monitoring infrastructure
3Measurement precision
If specialized expertise is required to interpret pipeline monitoring outputs, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides automated feedback by comparing measured hydraulic signals against expected operational patterns. When deviations indicating potential issues are detected, the system automatically generates alerts or reports, reducing the need for specialized manual interpretation while maintaining assessment accuracy
Solution Approach 2:
The monitoring system processes data in segmented operational phases (pump start, steady state, pump stop) and applies specific analysis methods to each phase. This segmentation simplifies the interpretation process by breaking down complex continuous data into manageable operational stages with characteristic patterns
Data Source
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AI summary
A rising main pipeline assessment system and method are described. The system comprises an analysis system, a monitoring system and a processing hub. The analysis system is configured to obtain data externally recorded on the pipeline and, from the externally recorded data, generate a steady state hydraulic model for the pipeline, the model defining expected performance zones for the pipeline under normal operating conditions and zone boundaries delineating normal and abnormal operating conditions for the pipeline, the analysis system being configured to record the model in a data repository of the processing hub. The monitoring system includes a pressure transducer that is connectable to the pipeline and configured to obtain measurements on the pipeline during operation of the pipeline and generate, for each of a plurality of predetermined time periods, a data record including minimum, maximum and mean measurements obtained in the determined time period, the monitoring system being configured to communicate the data record to the processing hub. The processing hub is configured to classify each received data record measurements according to its measurements and the performance zones of the model, the processing hub being configured to monitor the classified data records for each performance zone and generate an alarm upon identifying a predetermined pattern of classified data records.