Gas Flow Meter Baseline dP Learning for Diagnostic Accuracy
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Solution Overview
Problem
Existing gas metering systems for natural gas, such as rotary positive displacement meters, face challenges in maintaining accuracy over time due to wear and contamination, requiring frequent differential pressure (dP) testing, which is labor-intensive and may not detect performance degradation early enough to prevent measurement errors.
Innovation Solution
An automated system that learns a baseline dP characteristic over time, allowing for continuous monitoring and comparison of measured dP against a learned baseline, triggering alerts or repairs when thresholds are exceeded, and self-characterizes performance characteristics without operator intervention, reducing the need for portable test equipment and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual differential pressure testing is performed periodically by operators using portable manometers, then meter performance can be monitored, but the process is labor-intensive and may not detect performance degradation early enough
Solution Approach 1:
The system performs self-diagnosis by automatically comparing measured differential pressure against stored baseline characteristics to detect performance degradation without requiring external operators or portable test equipment. The metering system monitors its own performance continuously or at scheduled intervals, eliminating the need for manual testing while maintaining detection accuracy.
Solution Approach 2:
The patent replaces manual mechanical testing with automated electronic monitoring. Instead of operators physically connecting portable manometers and reading differential pressure values, the system uses electronic sensors and processors to automatically measure, store, and analyze differential pressure data, substituting mechanical manual operations with automated electronic systems.
2Reliability
If frequent manual differential pressure testing is performed to detect early performance degradation, then measurement accuracy can be maintained, but labor requirements and operational complexity increase
Solution Approach 1:
The system automatically monitors its own performance by continuously or periodically measuring differential pressure and comparing it against baseline characteristics stored in memory. This self-monitoring capability maintains measurement accuracy without requiring external operators to perform manual tests, thereby preserving reliability while simplifying operation.
Solution Approach 2:
The system enables continuous or frequent monitoring of differential pressure without the间断性 nature of manual testing. By automatically performing measurements and comparisons over extended periods, the system maintains high measurement accuracy and reliability without the operational complexity of coordinating frequent manual testing events.
3Adaptability or versatility
If portable manometers are used for differential pressure testing, then field testing is possible, but the equipment is large and heavy requiring operator transport
Solution Approach 1:
The patent extracts the testing functionality from external portable equipment and integrates it directly into the metering system itself. The differential pressure sensing, data storage, and analysis capabilities are built into the meter, eliminating the need to transport separate portable manometers to the field while maintaining full field testing capability.
Solution Approach 2:
The system combines the differential pressure measurement, baseline storage, and performance analysis functions into an integrated unit within the metering system. This merging of functions eliminates the need for separate portable test equipment, reducing the weight and complexity of field testing while preserving adaptability for field deployment.
4Measurement precision
If baseline differential pressure characteristics are established manually during installation, then performance comparison is possible, but the process requires operator intervention and plotting
Solution Approach 1:
The system replaces manual baseline establishment with automated electronic processes. Instead of operators physically plotting differential pressure values on charts, the system uses electronic sensors to measure baseline characteristics and stores them digitally in memory, enabling automated comparison without manual intervention while maintaining measurement precision.
Solution Approach 2:
The system creates an electronic copy of the baseline differential pressure characteristics and stores it in memory for future comparisons. This electronic copying replaces the physical chart plotting process, allowing automated retrieval and comparison of baseline data without requiring operators to manually recreate or reference physical plots.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables early and less labor-intensive identification of meter accuracy issues, reducing measurement errors and financial impacts by continuously monitoring meter performance, automating diagnostics, and self-characterizing performance characteristics, thus enhancing the accuracy of gas volume measurements.
Implementation Method 1
a differential pressure transducer that generates a differential pressure signal based on a difference between an inlet gas pressure and an outlet gas pressure
Implementation Method 2
a volume sensor that detects a rotation of impellers in the rotary flow meter and generates a volume signal that indicates a volume of the gas that flows through the meter
Data Source
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AI summary
Apparatus and associated systems and methods relate to automated learning of a baseline differential pressure (dP) characteristic to monitor the performance of a field-installed gas flow meter by comparing on-line dP measurements to the learned baseline dP characteristic. In an exemplary embodiment, a first baseline dP characteristic may be learned in a first mode over a first predetermined period of time according to a first set of learning criteria, and a second baseline dP characteristic may be learned in a second mode over a second predetermined period of time according to a second set of learning criteria. The first period of time may be substantially shorter than the second period of time. The first set of criteria may be substantially more relaxed than the second set of criteria. During the second mode, meter performance degradation may be diagnosed by comparing measured dP against the first baseline dP characteristic.