Hybrid Thermal and THz Sensing for Corrosion Under Fireproofing
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Solution Overview
Problem
Current methods for detecting corrosion under fireproof insulation (CUF) are inefficient and unreliable, often requiring extensive scaffolding and random insulation removal, which are costly and time-consuming.
Innovation Solution
A hybrid sensing system combining thermal imaging, terahertz radiation, and ambient condition sensors, coupled with machine learning algorithms, to predict and detect CUF non-destructively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical removal of insulation is used for inspection, then corrosion damage can be directly observed, but inspection cost and time increase significantly due to extensive scaffolding requirements
Solution Approach 1:
The patent replaces the mechanical inspection method (physical removal of insulation and visual examination) with a non-contact optical detection system using infrared thermography. This substitution eliminates the need for scaffolding and physical insulation removal while maintaining corrosion detection capability, thereby resolving the contradiction between detection accuracy and inspection efficiency.
Solution Approach 2:
The patent introduces infrared thermal imaging as an intermediary detection method that indirectly reveals corrosion conditions without direct physical contact. The infrared camera detects thermal anomalies caused by corrosion, serving as a mediator between the inspector and the hidden corrosion damage, thus avoiding the need for invasive inspection methods.
2Reliability
If random insulation removal is performed for inspection, then some corrosion areas may be detected, but the inspection reliability remains insufficient due to the hit-or-miss nature of the process
Solution Approach 1:
The patent performs preliminary thermal imaging inspection of the entire insulation surface before any physical removal. This preliminary action identifies suspicious areas that require further investigation, allowing inspectors to target specific locations rather than performing random removals. This significantly improves inspection reliability while reducing the time and material removed during detailed examination.
Solution Approach 2:
The patent utilizes thermal contrast visualization where corrosion areas appear as distinct thermal patterns (color variations in thermal images) compared to healthy insulation. This visual differentiation allows reliable identification of corrosion locations without random sampling, enabling comprehensive and reliable inspection of all assets.
3Ease of operation
If extensive scaffolding is deployed for visual inspection, then all equipment locations can be accessed, but construction expenses and inspection duration increase greatly
Solution Approach 1:
The patent replaces the complex mechanical scaffolding system with a simple infrared camera mounted on a tripod or handheld platform. This substitution maintains accessibility to all equipment locations through thermal imaging while dramatically reducing the complexity and cost of the inspection system setup.
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 enables accurate and efficient detection of CUF, reducing the need for extensive scaffolding and random insulation removal, thereby lowering costs and improving detection reliability.
Implementation Method 1
a first detection apparatus adapted to capture thermal images of the asset
Implementation Method 2
a second detection apparatus adapted to receive terahertz (THz) radiation from the asset
Data Source
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AI summary
A system for predicting and detecting of corrosion under fireproof insulation (CUF) in a metallic infrastructure asset (715) comprises a first detection apparatus (725) adapted to capture thermal images of the asset (715), a second detection apparatus (720) adapted to receive THz radiation from the asset (715), and at least one ambient condition sensor (735) for detecting environmental conditions at a location of the asset (715), a communication device coupled to the first and second detection apparatus (725, 720) and at least one ambient condition sensor (735) to receive data therefrom and adapted to communicate the received data, and a computing device communicatively coupled to the communication device and configured with instructions for executing a machine learning algorithm (730) taking as inputs the thermal image, THz and ambient condition data received from the communication device, and to output a CUF prediction regarding the asset (715).