Infrared-Visible Image Pairs for Asset Time-to-Failure Prediction

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Solution Overview

Problem

Existing asset management systems struggle to accurately predict maintenance intervals, leading to costly or disruptive failures and inefficient maintenance schedules, often requiring manual inspections with associated risks and inaccuracies.

Innovation Solution

A computer-implemented method using machine learning to analyze similarity coefficients between infrared and visible light images of assets, reconstructing images to a healthy condition, and predicting failure times based on historical data to optimize maintenance schedules.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual inspections are used to monitor asset conditions, then maintenance decisions can be made, but inspection risks and inaccuracies increase

Engineering Contradiction:

Improvemaintenance decision accuracyVSAvoidinspection risks

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent replaces manual mechanical inspections with automated image capture systems using visible light and infrared cameras. These systems objectively record asset conditions without human intervention, eliminating inspection risks while improving accuracy through consistent, repeatable measurements of thermal and visual parameters.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces image processing algorithms and machine learning models as intermediaries between the asset and the maintenance decision-making process. These intermediaries analyze thermal and visual image data to detect anomalies and predict failures, providing reliable maintenance recommendations without requiring direct human inspection of potentially hazardous assets.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If maintenance is performed more frequently to prevent failures, then asset reliability improves, but maintenance costs and downtime increase

Engineering Contradiction:

Improveasset availabilityVSAvoidmaintenance downtime

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent uses thermal and visual image analysis to detect early signs of asset degradation before failures occur. By identifying anomalies such as abnormal heat patterns or visual defects in advance, the system enables planned maintenance scheduling that prevents unexpected failures while avoiding unnecessary maintenance interventions, thereby optimizing asset availability and reducing downtime.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements a feedback loop where continuous image capture and analysis provide real-time information about asset conditions. This feedback enables dynamic adjustment of maintenance schedules based on actual asset health status rather than fixed intervals, allowing maintenance to be performed only when necessary and optimizing the balance between reliability and downtime.

Inventive Principle:

Principle #23Feedback

3Loss of information

If traditional visible light images are used for asset inspection, then equipment can be monitored, but thermal anomalies and hidden defects remain undetected

Engineering Contradiction:

Improvedetection completenessVSAvoidinspection system complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent merges visible light imaging and infrared thermal imaging into a unified inspection system. By capturing both types of images simultaneously or sequentially and analyzing them together, the system achieves comprehensive detection of asset conditions, identifying both visual defects and thermal anomalies that would be invisible to either modality alone, thereby eliminating information loss without requiring separate inspection systems.

Inventive Principle:

Principle #5Merging (Combining)

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 predicts asset failure times, reducing maintenance frequency, minimizing downtime, and enhancing safety by automating inspections and scheduling maintenance based on evidence-based data.

Implementation Method 1

Infrared (IR) light is a type of radiant electromagnetic energy that is invisible to human vision but can be felt or measured as heat. Capturing IR images with special devices, such as thermal imagery cameras, adds additional details of anomalies

Methodology Applied

Scientific EffectThermal radiation: Thermal Radiation

Data Source

PatentUS12450728B2Asset maintenance prediction using infrared and regular images

Publication Date: 2025.10.21 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method for predicting a time to a failure condition of an asset includes a first model, trained to reconstruct input image pairs to resemble a healthy condition asset. Similarity coefficients are generated for respective historical image pairs that include a visible-light and infrared light image by use of the reconstructed image pair by the first model as a similarity base and the respective historical image pairs include a timestamp of image capture. A second model is trained to predict a time to a failure condition of the asset based on similarity coefficients and timestamps of real-time image pair capture, timestamps of an asset failure condition, and similarity coefficients of the respective historical images. Responsive to receipt of a first real-time image pair, the method computes a predicted time to the failure condition of the asset.