Bi-directional Prover Sensor Diagnostics via Meter Factor Trends
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Solution Overview
Problem
Current systems for monitoring the health of sensors in bi-directional sphere provers in pipeline systems do not account for historical trends in meter factors and ratios, leading to potential errors in flowmeter calibration and inefficient maintenance.
Innovation Solution
A Data Acquisition and Monitoring System that includes multiple detector switches, pulse counters, and a processor to calculate and compare meter factors, providing diagnostic information on sensor health by analyzing historical data and current readings to determine if sensor replacements are needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor monitoring systems are used in bi-directional sphere provers, then the system structure remains simple, but measurement precision and reliability of flowmeter calibration deteriorate due to inability to detect historical trends in meter factors
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing meter factor data from multiple prover passes before calibration decisions are needed. This historical data accumulation enables trend analysis that predicts sensor degradation, allowing proactive calibration maintenance before accuracy deteriorates beyond acceptable thresholds.
Solution Approach 2:
The monitoring system implements feedback by continuously comparing current meter factor readings against historical trends and established thresholds. When deviations exceed predetermined limits, the system generates alerts and diagnostic information, creating a closed-loop feedback mechanism that maintains calibration accuracy through timely intervention.
2Reliability
If continuous monitoring of all sensor parameters is implemented, then reliability of calibration improves, but loss of time for data processing and analysis increases
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on critical parameters and threshold violations rather than continuously analyzing all sensor data. It processes only the necessary subset of data that indicates potential calibration issues, avoiding excessive computation on normal operating conditions.
Solution Approach 2:
The monitoring system performs self-service by automatically analyzing collected data, comparing readings against historical trends, and generating diagnostic conclusions without requiring continuous human intervention. This automated self-assessment reduces the time operators need to spend on manual data review while maintaining reliable calibration monitoring.
3Loss of information
If historical trend analysis is added to current monitoring systems, then diagnostic capability improves, but device complexity increases due to additional data storage and processing requirements
Solution Approach 1:
The system extracts only the essential diagnostic information from historical data that is necessary for calibration assessment. It identifies and focuses on key parameters such as meter factor trends, rate of change, and threshold violations, separating critical diagnostic data from redundant information to minimize storage and processing complexity.
Solution Approach 2:
The system transforms raw historical sensor data into meaningful diagnostic parameters by calculating derived metrics such as meter factor trends, rates of change, and deviation from expected values. This parameter transformation converts large volumes of raw data into compact, actionable diagnostic information that improves detection capability without proportionally increasing complexity.
Data Source
AI summary
The invention provides generally methods and systems in a bi-directional sphere prover for generating diagnostic information by calculating and using multiple meter factors (MF) from four detector switches. A Data Acquisition and Monitoring System gathers the signals of the four detector switches and calculates the base prover volume (BPV) for each section of the prover based on these readings. Then, the different base prover volumes are used to create multiple meter factors for each section of the prover to derive the diagnostic information. The Data Acquisition and Monitoring System displays, archives, and trends the meter factors. Depending upon what particular meter factor ratios are within the acceptable limit, the correct detector switch can be diagnosed and fixed without a substantial amount of down time for the prover. Trend lines for meter factors can also be analyzed for different process fluids that are sent through the prover.