Asset Intelligence Interface for Corrosion Failure Prediction
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Solution Overview
Problem
Industrial facilities, such as oil and gas production plants, face challenges in monitoring and predicting the status of assets like pipes due to the large volume of data from sensors and the complexity of corrosion processes, making it difficult to identify potential failures and take corrective action before they occur.
Innovation Solution
An interface that provides improved visualization of data by allowing users to toggle between sensor data and predictive model data, using a graphical display to show the current and future status of assets, including corrosion profiles, enabling users to quickly ascertain the status of assets without reviewing detailed sensor data and predicting time to failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed sensor data is reviewed to ascertain asset status, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical asset that mirrors its state and behavior. This digital replica allows users to assess asset status, view corrosion profiles, and predict failures without examining raw sensor data directly. The digital twin consolidates complex sensor readings into intuitive visual representations, maintaining measurement precision while dramatically reducing the time required for status assessment.
2Reliability
If comprehensive sensor data is collected to monitor asset condition, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent introduces a digital twin as an intermediary between the complex sensor network and the user. Instead of directly presenting raw sensor data or complex corrosion models to users, the digital twin serves as a mediator that translates comprehensive technical data into intuitive visual representations. This intermediary layer maintains high reliability by accurately reflecting asset status while simplifying the user interface and reducing perceived system complexity.
Solution Approach 2:
By creating a virtual digital twin that replicates the physical asset's behavior and state, the system consolidates complex multi-sensor data into a single coherent model. This copy allows comprehensive monitoring of asset condition, corrosion profiles, and failure predictions without requiring users to navigate complex data structures or understand intricate sensor networks.
3Productivity
If predictive modeling is implemented to forecast asset failure, then productivity is improved through proactive maintenance, but device complexity increases
Solution Approach 1:
The digital twin serves as a virtual laboratory where predictive modeling can be performed without affecting the physical asset. By simulating future asset states, corrosion progression, and potential failures in the digital replica, the system enables proactive maintenance planning. Users can observe predicted failure timelines and corrosion scenarios in the digital twin, allowing productivity improvements through early intervention while keeping the predictive modeling infrastructure contained within the virtual environment.
Data Source
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Figure 3
Figure 4A
AI summary
A method can include receiving data characterizing a first property of an asset over a first time period. The method can also include receiving data characterizing user interaction with an interactive graphical object. The method can further include determining, by a predictive model, data characterizing the first property of the asset over a second time period. The determining can be based in part on the received data characterizing the user interaction. The method can also include rendering, in a graphical display space, one or more of a first plot of the received data characterizing the first property over the first time period and a second plot of the determined data characterizing the first property over the second time period.