Valve Passing Detection Using Acoustic-Thermal Data Fusion
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Solution Overview
Problem
Unintentional passing of gases through valves in oil and gas plants leads to environmental hazards and significant business losses, as gases not meant for burning escape into the atmosphere, contributing to air pollution and impacting human health and wildlife.
Innovation Solution
A system utilizing acoustic emission sensors and thermal cameras, combined with data fusion and machine learning models, to detect and quantify defects in valves, enabling timely corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual inspection methods are used to detect valve defects, then operational simplicity is maintained, but detection precision and reliability are insufficient
Solution Approach 1:
The patent combines multiple sensing modalities (acoustic emission sensors, thermal cameras, infrared sensors) into an integrated monitoring system. This merging of different detection technologies enables comprehensive valve health assessment through data fusion, simultaneously improving detection precision across multiple defect types while managing system complexity through coordinated sensor deployment
Solution Approach 2:
The monitoring system is designed to detect multiple valve defect types (passing valves, stuck valves, degradation) using a unified multi-sensor platform. The system performs various detection functions including acoustic anomaly detection, thermal imaging, and infrared analysis, making it a universal solution for comprehensive valve health monitoring rather than requiring separate specialized systems
2Reliability
If multiple sensors are deployed to improve detection accuracy, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms where sensor data is continuously processed and analyzed to provide real-time valve status information. The data fusion algorithm integrates inputs from multiple sensors with feedback loops that adjust detection parameters and provide corrective actions, improving reliability through continuous monitoring while managing complexity through systematic feedback processing
Solution Approach 2:
The patent introduces data fusion algorithms and processing systems as intermediaries between the multiple sensors and the final detection output. This intermediary layer integrates and harmonizes data from acoustic emission sensors, thermal cameras, and infrared sensors, transforming complex multi-source inputs into reliable unified valve status assessments, thereby improving detection reliability while managing the complexity of coordinating multiple sensors
3Productivity
If automated detection systems are implemented, then productivity and response time are improved, but device complexity and initial costs increase
Solution Approach 1:
The monitoring system enables self-service operation through automated data collection, processing, and analysis capabilities. The system autonomously detects valve defects, generates alerts, and provides corrective action recommendations without requiring constant human intervention, thereby improving productivity and operational efficiency while managing complexity through automated self-monitoring and self-diagnosis functions
Solution Approach 2:
The patent replaces manual mechanical inspection methods with automated electronic sensing and data processing systems. Instead of physical valve checking by operators, the system uses acoustic emission sensors, thermal cameras, and infrared sensors coupled with automated analysis algorithms, substituting mechanical human labor with electronic automation to improve productivity while managing the complexity transition from manual to automated systems
4Loss of substance
If comprehensive monitoring is performed to reduce environmental harm, then loss of substance is reduced, but use of energy increases
Solution Approach 1:
The system performs preliminary detection and warning before significant valve failures or gas leaks occur. By continuously monitoring valve health indicators through multiple sensors and detecting early signs of passing valves or degradation, the system enables proactive maintenance actions that prevent substantial gas loss, thereby reducing loss of substance while managing energy consumption through early intervention rather than reactive emergency responses
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
Accurately identifies passing valves and their defects, reducing resource waste and environmental harm by allowing for prompt intervention and maintenance.
Implementation Method 1
an acoustic emission sensor configured to detect acoustic emissions from a valve in a pipe system
Implementation Method 2
an infrared camera configured to capture thermal images of the valve
Data Source
AI summary
Systems and methods for detecting passing valves include an acoustic emission sensor configured to detect acoustic emissions from a valve in a pipe system; an infrared camera configured to capture thermal images of the valve; and a computer system. The passing valve can be detected by obtaining acoustic emission data from the acoustic emission sensor and infrared thermography data from the infrared camera; generating fused data by fusing together the acoustic emission data and the infrared thermography data; determining that the valve is a passing valve using a machine learning model that takes as input the fused data and generates as output the determination; and determining a severity of the passing valve, a defect causing the passing valve, and a location of the defect based on the fused data.