Industrial Asset Inspection Planning With Smart Data Tagging
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Solution Overview
Problem
Traditional industrial asset inspection methods are time-consuming, error-prone, and inefficient due to the manual correlation and fusion of disparate data sources, making it difficult to accurately and efficiently assess the integrity of industrial assets, especially in complex and hard-to-reach environments.
Innovation Solution
An industrial asset inspection platform that accesses meta-data to generate an inspection plan, associates sensors with points of interest, receives inspection data, and executes a smart tagging algorithm to automate the correlation and analysis of data, facilitating the generation of accurate and efficient inspection reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection and data correlation methods are used, then human inspectors can analyze asset integrity data, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical inspection processes with automated computational systems. The smart tagging algorithm automatically correlates sensor data with asset locations, replacing the mechanical process of manual data correlation and fusion that human inspectors performed, thereby reducing inspection time while maintaining or improving accuracy
Solution Approach 2:
The inspection system performs self-service through automated data processing. The platform automatically generates inspection plans, executes smart tagging algorithms to correlate data with asset locations, and produces inspection reports without requiring continuous human intervention, enabling the system to serve itself in the inspection process
2Reliability
If manual data correlation and fusion are performed, then inspection data can be analyzed, but errors are introduced and efficiency is reduced
Solution Approach 1:
The patent replaces manual data correlation processes with automated computational algorithms. The smart tagging algorithm systematically fuses sensor data with asset location information, eliminating human errors introduced during manual data correlation while significantly improving inspection efficiency through automated processing
Solution Approach 2:
The system implements feedback mechanisms where inspection data is automatically processed and correlated with asset locations, and the results are used to generate refined inspection reports. This automated feedback loop ensures consistent and reliable data analysis without the errors associated with manual processing
3Ease of operation
If human inspectors manually examine asset locations, then inspection can be performed, but it becomes cumbersome and error-prone
Solution Approach 1:
The patent replaces manual inspection operations with automated systems. The platform automatically generates inspection plans, executes smart tagging to correlate data with asset locations, and produces reports, making the inspection process easier to operate while simultaneously improving reliability by eliminating human errors in data correlation and analysis
Data Source
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.