Cloud-Based CUI Detection Using Sensor Fusion and ML
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Solution Overview
Problem
Corrosion under insulation (CUI) is challenging to detect due to its hidden nature, leading to costly and time-consuming inspections, with existing methods often resulting in false positives and negatives, and there is a need for improved predictive risk assessment tools to determine CUI damage and maintain facility integrity.
Innovation Solution
A cloud-based system utilizing infrared cameras, smart mounts with wireless communication and sensor modules, and machine learning algorithms, including deep recurrent neural networks, to capture thermal images and ambient data, providing accurate CUI prediction and detection by combining sensor fusion and time-based analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If NDT techniques such as infrared thermography are used to detect CUI, then detection capability is improved, but false positives and false negatives increase due to large number of variables
Solution Approach 1:
The patent introduces multiple intermediary sensors (ambient sensors, structural probe sensors, corrosion sensors) that act as mediators between the insulation and the detection system. These sensors capture specific physical quantities (temperature, humidity, structural integrity, corrosion indicators) to provide indirect but more reliable information about CUI conditions, reducing false detections while improving overall detection capability
Solution Approach 2:
The system combines data from multiple different sensor types (infrared cameras, ambient sensors, structural probes, corrosion sensors) to create a composite detection approach. This multi-sensor fusion creates a more robust detection system that compensates for the limitations of individual sensors, improving both detection capability and precision by cross-validating findings across multiple measurement modalities
2Measurement precision
If insulation is removed for visual inspection to detect CUI, then detection accuracy is improved, but inspection time and cost increase significantly
Solution Approach 1:
The system performs preliminary detection using non-invasive sensors (infrared cameras, ambient sensors, structural probes) before any insulation removal. These preliminary measurements identify potential CUI locations and assess risk levels, allowing inspectors to target only high-probability areas for closer examination, thereby reducing overall inspection time while maintaining high detection accuracy
Solution Approach 2:
The patent replaces the mechanical process of insulation removal with sensor-based detection methods. Instead of physically removing insulation to visually inspect for corrosion, the system uses infrared thermography, ambient sensing, structural probing, and corrosion sensors to detect CUI conditions through the insulation or at minimal intervention points, dramatically reducing inspection time and labor requirements
3Area of stationary object
If localized visual inspections are performed on elevated pipes requiring scaffolding, then inspection coverage is improved, but operational complexity and cost increase
Solution Approach 1:
The sensor system is designed to be universally applicable to various pipe configurations and locations. The infrared cameras, ambient sensors, structural probes, and corrosion sensors can detect CUI on elevated pipes, ground-level pipes, and different insulation types without requiring specialized equipment or complex setup procedures, thereby expanding inspection coverage while maintaining system simplicity
Solution Approach 2:
The system enables self-service inspection capabilities where sensors automatically monitor and detect CUI conditions without requiring complex manual intervention or specialized scaffolding setups. The automated sensing and data processing allow facilities to perform inspections independently, reducing the need for complex external inspection systems and specialized equipment
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
The system enhances detection accuracy by reducing noise and confounding variables, allowing for real-time prediction and verification of CUI, enabling more intelligent machine learning models and effective maintenance scheduling, thereby reducing costs and ensuring facility safety.
Implementation Method 1
at least one infrared camera positioned to capture thermal images of the asset
Implementation Method 2
the computing device configured with instructions for executing a machine learning algorithm taking as inputs thermal image data, ambient condition data and CUI-related data from the probe sensor
Data Source
AI summary
A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.