Mass Flow Meter Self-Diagnostic Correction for Sensor Deviations
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Solution Overview
Problem
Mass flow meters with self-diagnostics functions face challenges in accurately detecting abnormalities when measuring different fluids at varying temperatures and pressures, due to individual differences in sensor unit responses, leading to incorrect judgments and reduced measurement accuracy.
Innovation Solution
A mass flow meter with two flow sensor units of identical specifications, where the control unit executes a learning function by measuring flow rate deviations at various flow rates and temperatures, calculates correction values to eliminate individual response differences, and uses these to determine if a malfunction has occurred based on a threshold value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a mass flow meter uses two flow sensor units with identical specifications to enable self-diagnostics, then the ability to detect abnormalities is improved, but individual differences in sensor responses lead to incorrect judgments when measuring different fluids at varying temperatures and pressures
Solution Approach 1:
The system performs preliminary learning measurements at multiple flow rates and temperatures before actual measurement to establish baseline characteristics of each sensor unit. This preliminary action captures individual sensor differences under various conditions, enabling subsequent correction of abnormality detection judgments.
Solution Approach 2:
The system varies measurement parameters including flow rate and temperature during the learning phase to capture sensor responses across different operating conditions. By storing correction values for multiple parameter combinations, the system adapts to individual sensor characteristics under varying measurement conditions.
2Measurement precision
If the control unit measures flow rate deviations at various flow rates and temperatures to calculate correction values, then measurement accuracy across different conditions is improved, but the complexity of the control unit increases
Solution Approach 1:
The control unit separates the measurement process into distinct phases: learning measurements at multiple flow rates and temperatures to establish baseline characteristics, and actual measurements using stored correction values. This segmentation allows complex multi-parameter learning without requiring continuous complex processing during operation.
Solution Approach 2:
The control unit performs preliminary learning measurements and stores correction values before actual measurement operations. This preliminary action transfers computational complexity from ongoing measurements to an initial setup phase, simplifying the control unit's workload during normal operation.
3Reliability
If the system uses correction values to eliminate individual response differences, then the reliability of self-diagnostics is improved, but the time required for learning measurements increases
Solution Approach 1:
The system performs learning measurements at multiple flow rates and temperatures, which is more than a single-condition learning would require. This excessive action captures comprehensive sensor characteristics, improving the reliability and适用范围 of correction values across varying operating conditions.
Solution Approach 2:
The system varies flow rate and temperature parameters during learning measurements to establish comprehensive baseline data. By capturing sensor responses across multiple parameter combinations, the system creates more robust correction values that remain reliable under varying measurement conditions.
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 approach enables reliable self-diagnostics by ensuring that flow rate deviations are within acceptable limits, allowing for sensitive detection of abnormalities and maintaining high measurement accuracy across different fluid types and conditions.
Implementation Method 1
when the pair of sensor wires generates heat by being impressed a predetermined voltage (or flowed a predetermined current), heat generated from the sensor wires is taken by the fluid which flows through the sensor tube
Implementation Method 2
heat generated from the sensor wires is taken by the fluid which flows through the sensor tube. As a result, the fluid which flows through the sensor tube is heated
Data Source
AI summary
In a mass flow meter comprising two flow sensor units with identical specifications, flow rate deviations between these two flow sensor units are initially measured at various mass flow rates under a circumstance having the same fluctuating factors as those when a mass flow is actually measured. Subsequently, based on these flow rate deviations, a correction value for matching the values of the mass flow rates measured by these two flow sensor units is calculated and stored in a data storage device. Thereafter, when measuring a mass flow rate, a flow rate deviation, from which the influence by the individual difference in the response to a fluctuating factor between these two flow sensor units has been removed, is calculated by correcting a measured value based on the correction value. The existence or non-existence of an occurrence of a malfunction is judged based on whether the flow rate deviation exceeds a predetermined threshold value t or not.