Industrial Asset Inspection Data Tagging for Integrity Review
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Solution Overview
Problem
Conventional industrial asset inspection methods are cumbersome, error-prone, and inefficient due to manual data correlation and fusion of disparate data sources, making them time-consuming and unsuitable for complex or hard-to-reach environments.
Innovation Solution
An industrial asset inspection platform that generates an inspection plan based on meta-data, associates sensors with points of interest, and executes a smart tagging algorithm to automate the correlation and collation of inspection data, generating accurate and efficient inspection reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data correlation and fusion methods are used, then human inspectors can analyze asset integrity data, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables automated self-service through the smart tagging algorithm that automatically correlates inspection data with asset components without human intervention. The algorithm independently processes sensor data, identifies points of interest, and generates inspection reports, eliminating the need for manual data correlation while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual inspection process with an automated computational system. The smart tagging algorithm substitutes human inspectors by using computational methods to correlate disparate data sources, fuse sensor information, and generate inspection summaries, thereby reducing both time consumption and human error.
2Productivity
If manual inspection methods are used, then inspectors can locate defects, but the process is cumbersome and inefficient for complex assets
Solution Approach 1:
The inspection platform provides universal functionality by integrating multiple inspection capabilities into a single system. The smart tagging algorithm can handle various data types (sensor data, images, videos) and apply to different asset types (pipelines, towers, vessels), making the system versatile and efficient for complex assets without requiring separate manual processes for each asset type.
Solution Approach 2:
The patent introduces an intermediary smart tagging algorithm that mediates between raw inspection data and final inspection reports. This algorithm acts as a bridge that automatically correlates disparate data sources, fusion sensor information, and generates coherent inspection summaries, thereby simplifying the overall inspection process for complex assets.
3Extent of automation
If automated smart tagging algorithm is used, then data correlation is automated and efficient, but the system requires complex data processing capabilities
Solution Approach 1:
The patent segments the complex data processing task into manageable components through the smart tagging algorithm. The system divides the inspection process into distinct stages: data collection from multiple sensors, automatic correlation of data with asset components, identification of points of interest, and generation of inspection reports. This segmentation makes the automation process more manageable and less complex while maintaining high efficiency.
Data Source
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AI summary
In some embodiments, a meta-data inspection data store may contain hierarchical components and subcomponents of an industrial asset and define points of interest. An industrial asset inspection platform may access that information and generate an inspection plan, including an association of at least one sensor type with each of the points of interest. The platform may then store information about the inspection plan in an inspection plan data store and receive inspection data (e.g., from a manual inspection, from an inspection robot, from a fixed sensor, etc.). A smart tagging algorithm may be executed to associate at least one point of interest with an appropriate portion of the received inspection data based on information in the inspection plan data store.