Automated Diagnostic Image Linking to Specific Maintenance Assets
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Solution Overview
Problem
Industrial maintenance workflows face challenges in obtaining appropriate contextual information, particularly the identification of specific maintenance assets from machine-health diagnostic images, leading to inefficiencies and errors in asset identification.
Innovation Solution
Utilizing machine learning techniques, including deep learning neural networks, to automatically identify maintenance assets from machine-health diagnostic images such as thermal, visible-light, and acoustic images, applying object detection, image classification, and OCR to generate asset identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification methods are used to link diagnostic images to maintenance assets, then the process requires human intervention and contextual knowledge, but the time consumption and error rates increase significantly
Solution Approach 1:
The system enables automatic self-identification of maintenance assets through machine learning models that process diagnostic images and extract asset identifiers without human intervention. The model autonomously links images to assets by analyzing visual features and comparing them against a database of known assets.
Solution Approach 2:
Manual mechanical processes of asset identification are replaced with automated computer vision and machine learning systems. The ML model substitutes human analysts by automatically processing thermal and visible light images to identify and link assets, eliminating the need for manual image review and asset matching.
2Productivity
If automated machine learning systems are deployed to identify maintenance assets from diagnostic images, then identification speed and consistency improve, but system complexity and initial implementation costs increase
Solution Approach 1:
The machine learning system is designed to handle multiple asset types and diagnostic image formats through a single unified model. The system can process thermal images, visible light images, and various asset categories using the same underlying technology platform, reducing the need for multiple specialized systems.
Solution Approach 2:
The system performs preliminary processing of diagnostic images by pre-processing thermal and visible light images to enhance features before main identification. Asset databases are pre-populated with reference images and metadata, enabling faster real-time matching and reducing computational complexity during actual identification operations.
Data Source
AI summary
Methods and apparatuses that utilize machine learning techniques to identify maintenance assets using sets of machine-health diagnostic images and link individual machine-health diagnostic images to the identified maintenance assets are described. The sets of machine-health diagnostic images may include a set of thermal images, a set of visible-light images, and/or a set of acoustic images. An identified maintenance asset may comprise an individual machine associated with a unique asset identifier. A diagnostic image linking system may acquire machine-health diagnostic images, apply object detection and other computer vision techniques to identify a particular machine within the machine-health diagnostic images, determine machine properties for the particular machine, generate a feature vector using the machine properties, select machine learning models corresponding with maintenance assets, generate predicted answers using the machine learning models, and generate an asset identifier for the particular machine based on the predicted answers.