Plant Asset Visualization for Corrosion Loop Risk Prediction
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Solution Overview
Problem
Process engineering plants face significant productivity losses due to corrosion and degradation of assets, leading to unplanned downtime, as existing asset management strategies rely on unreliable data and reactive maintenance approaches, lacking real-time monitoring and predictive capabilities.
Innovation Solution
A machine-learning based asset monitoring system that identifies corrosion loops and damage mechanisms using AI models, extracts engineering data from disparate systems, and generates real-time multidimensional visualizations for proactive maintenance, integrating data from various sources to provide end-to-end visibility and improve asset reliability and productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive maintenance strategies are used to reduce asset degradation, then asset life can be extended, but productivity is greatly impacted due to unplanned downtime and the strategies can only determine damage when it becomes evident
Solution Approach 1:
The system performs preliminary actions by continuously monitoring asset conditions and predicting future failures before they occur. The ML models analyze current asset states and operational data to forecast potential issues, enabling maintenance to be scheduled in advance rather than reacting to actual failures, thus preventing unplanned downtime while maintaining productivity
Solution Approach 2:
The system implements continuous feedback loops where asset monitoring data is constantly fed into ML models that update predictions and recommendations. This real-time feedback mechanism allows the system to adapt to changing asset conditions, improving reliability predictions while enabling proactive maintenance scheduling that maintains productivity
2Productivity
If expensive proactive maintenance strategies are adopted to reduce downtime and increase asset life, then productivity can be improved, but the reliability of these strategies largely depends on the quality of data from disparate systems and the ability to build good models
Solution Approach 1:
The system merges data from multiple disparate sources including operational systems, maintenance management systems, and asset monitoring systems into a unified data platform. This integration consolidates previously scattered data sources, improving data quality and consistency for ML model training while enabling reliable proactive maintenance strategies that enhance productivity
Solution Approach 2:
The system transforms raw data from disparate systems into standardized parameters and features suitable for ML analysis. By changing the form and structure of input data through normalization, feature engineering, and parameter transformation, the system improves data quality and model reliability, enabling effective proactive maintenance strategies
3Measurement precision
If manual data collection and model building processes are used, then data quality can be improved, but the process is time-consuming and strategies can only determine damage when it becomes evident
Solution Approach 1:
The system implements self-service capabilities where ML models automatically collect, process, and analyze data from multiple sources without requiring manual intervention. The automated model building and continuous monitoring processes maintain high data quality while detecting asset damage early in its development, preventing the time loss associated with manual processes and delayed damage detection
4Reliability
If data from disparate systems is integrated to improve model quality, then asset monitoring reliability can be improved, but the complexity of integrating multiple data sources increases
Solution Approach 1:
The system introduces intermediary components including data integration layers, standardized data models, and API gateways that mediate between disparate data sources and the ML analytics platform. These intermediaries simplify the integration process, maintaining monitoring reliability by ensuring data quality while reducing the apparent complexity for end users through unified interfaces
Data Source
AI summary
A machine learning (ML) based asset monitoring system that automatically determines damage mechanisms (DMs) and generates automatically updated visualizations of assets that include equipment and lines of a processing plant is disclosed. The asset monitoring system is communicatively coupled to the assets of the plant and continuously receives process parameters associated with the various processes and equipment in the plant. Corrosion loops (CLs) are identified and automatically demarcated by the asset monitoring system. DMs are predicted for each of the assets using a ML model based on the process parameters and the corrosion loops. The data regarding the DMs, CLs and the process parameters are used to obtain equipment risk rankings for the assets. Multi-dimensional visualizations of the assets that display the state of the plant assets in real-time are generated.