Plant Asset Failure Prediction Engine With Hybrid Model Integration
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Solution Overview
Problem
Existing methods for predicting plant asset failures in industrial processing plants are limited by their fixed structure and calculation methods, lacking the ability to integrate diverse data sources and models, which impede accurate and proactive maintenance.
Innovation Solution
A computer-based system that configures and deploys prediction models using a combination of diverse calculators and data sources, enabling real-time monitoring and detection of asset failures through a holistic approach that integrates first-principles and data-driven models, allowing for flexible data structures and extensible interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed structure and calculation method are used for predicting plant asset failures, then the system is simple to implement, but the prediction quality and accuracy are limited
Solution Approach 1:
The patent implements a dynamic prediction system where the engine can select and apply different calculation methods (first-principles models, data-driven models, machine learning models) based on the specific asset and available data. This dynamic approach allows the system to adapt to different scenarios rather than using a fixed calculation method, thereby improving prediction quality while managing complexity through structured flexibility.
Solution Approach 2:
The prediction engine is designed as a universal platform that can handle multiple types of assets (process equipment, mechanical equipment, electrical equipment) and integrate diverse data sources (sensor data, operational data, maintenance data). This multi-functional design allows a single system to serve various prediction needs across different asset types, improving overall prediction quality without requiring separate fixed systems for each asset category.
2Measurement precision
If diverse data sources and models are integrated into the prediction engine, then the prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the prediction system into distinct modular components: data acquisition modules for different data sources, separate calculation method modules (first-principles, data-driven, machine learning), and an integration layer that coordinates them. This segmentation allows diverse data sources and models to be integrated systematically, improving prediction accuracy while managing complexity through clear module boundaries and standardized interfaces.
Solution Approach 2:
The prediction engine acts as an intermediary layer between diverse data sources/models and the asset management system. It provides standardized interfaces and data formats that enable integration of heterogeneous sources without requiring complex point-to-point connections. This intermediary approach improves prediction accuracy by enabling diverse integration while reducing system complexity through standardized communication protocols.
3Adaptability or versatility
If multiple calculation methods are applied to different prediction models, then the comprehensive prediction capability improves, but the computational complexity increases
Solution Approach 1:
The system dynamically selects and applies appropriate calculation methods based on the specific prediction task, asset type, and available data. Rather than applying all calculation methods to all models, the engine adapts the computational approach to match the requirements of each prediction model, thereby improving comprehensive prediction capability while managing computational complexity through intelligent method selection.
Solution Approach 2:
Different calculation methods are applied locally to specific prediction models based on their requirements. For example, first-principles models may use physics-based calculations, while data-driven models use statistical or machine learning approaches. This localized application of appropriate calculation methods improves overall prediction capability without requiring all systems to handle all types of computational complexity.
Data Source
AI summary
Computer system and method builds and deploys a custom industrial (chemical) processing plant asset failure prediction engine that integrates disparate calculation methods and information (data) sources. The diverse calculation methods improve quality and accuracy of the asset failure predictions by embedding domain knowledge and providing a holistic assessment of plant assets.