Non-Intrusive Turbine Flowmeter Sensing for Wear Diagnostics
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Solution Overview
Problem
Turbine flowmeters suffer from mechanical wear and damage due to exposure to hydrogen and bio-gas, leading to inaccurate measurements and potential malfunctions, necessitating costly and time-consuming manual inspections.
Innovation Solution
Non-intrusive sensing and diagnostics using non-invasive sensors to monitor turbine flowmeters, capturing acoustic and acceleration signals through the meter's wall, performing time-frequency analysis, and employing multi-sensor fusion to detect anomalies and predict maintenance needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to detect mechanical fatigue in turbine meters, then inspection thoroughness can be ensured, but operational downtime increases and inspection efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical inspection with automated optical sensing systems. Cameras and image processing algorithms capture and analyze meter components remotely, eliminating the need for physical disassembly and manual examination while maintaining high detection accuracy for mechanical fatigue and defects.
Solution Approach 2:
The patent introduces an intermediary optical sensing system that acts as a mediator between the meter components and inspectors. Image capture devices and processing software serve as intermediaries to detect mechanical fatigue, cracks, and defects without requiring direct physical contact or operational shutdown.
2Measurement precision
If intrusive pulse pickup assemblies are used for measurement, then measurement functionality is achieved, but mechanical wear and measurement accuracy degradation occur over time
Solution Approach 1:
The patent replaces intrusive mechanical pulse pickup assemblies with non-contact optical sensing systems. Optical sensors detect turbine rotor position and flow characteristics through transparent meter walls, eliminating mechanical contact points that cause wear and accuracy degradation while maintaining reliable flow measurement capability.
Solution Approach 2:
The patent extracts the measurement function from the intrusive mechanical pulse pickup assembly and relocates it to external optical sensors. This separation removes the source of mechanical wear and contact-related accuracy degradation while preserving the essential flow measurement functionality through non-contact optical detection.
3Reliability
If periodic manual inspections are performed to identify defective devices, then detection capability is maintained, but inspection efficiency decreases and maintenance timing is delayed
Solution Approach 1:
The patent implements continuous automated optical monitoring of turbine meters, replacing periodic manual inspections. The optical sensing system continuously captures images and analyzes meter component conditions, enabling real-time defect detection and eliminating gaps between inspection cycles while maintaining high detection capability.
Solution Approach 2:
The patent enables the inspection system to perform self-service through automated image capture and analysis. The optical sensing system and processing algorithms automatically detect defects, assess mechanical fatigue, and generate maintenance alerts without human intervention, dramatically improving inspection efficiency while maintaining reliable defect detection.
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
Enables real-time, accurate monitoring and predictive maintenance, reducing downtime and improving measurement reliability by identifying issues before they cause significant damage.
Implementation Method 1
capturing acoustic and acceleration signals through the meter's wall
Implementation Method 2
capturing acoustic and acceleration signals through the meter's wall
Data Source
AI summary
Methods and systems for non-intrusive sensing and diagnostics of a turbine flowmeter can involve monitoring a turbine flowmeter with non-invasive sensors, extracting blade frequencies associated with turbine blades based on a flow rate, detecting pressure wave frequencies associated with the turbine flowmeter's internal moving parts, and employing multi-sensor fusion with respect to sensor data generated from non-invasive sensors including the extracted blade frequencies and the pressure wave frequencies to identify a health condition of the turbine flowmeter and to predict maintenance for the turbine flowmeter. The non-intrusive sensors can be installed in proximity to positions of moving parts of the turbine flowmeter such as turbine blades, bearings, rotor, and so on.