Bayesian Fault Diagnosis for Process Engineering Installations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for fault diagnosis in process engineering plants are inadequate, as model-based approaches require extensive system understanding and are computationally intensive, while signal-based methods struggle to assign faults to specific components due to lack of data and potential noise in measurements.
Innovation Solution
A hybrid approach combining model-based and signal-based methods using a probabilistic physical model created from engineering information and Bayesian inference to determine fault probabilities, leveraging bond graphs for a standardized representation of plant components and their interconnections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based approaches are used for fault diagnosis, then diagnostic accuracy is improved, but computational complexity and system understanding requirements increase
Solution Approach 1:
The system segments the complex diagnostic problem into modular Bayesian network components, where each node represents a specific fault hypothesis or measurement. This segmentation allows the computational complexity to be distributed and managed through localized probability calculations rather than requiring a single complex model to process all data simultaneously.
Solution Approach 2:
The patent introduces Bayesian probability theory as an intermediary framework that bridges raw measurement data and diagnostic conclusions. The Bayesian network acts as a mediator structure that systematically combines prior knowledge with current measurements, reducing the computational burden compared to direct model-based analysis while maintaining diagnostic accuracy.
2Device complexity
If signal-based methods are used for fault diagnosis, then computational burden is reduced, but ability to assign faults to specific components deteriorates
Solution Approach 1:
The system performs preliminary action by pre-defining the Bayesian network structure with all possible fault hypotheses and their relationships to measurements before actual diagnosis occurs. This preliminary setup includes encoding engineering knowledge about component-fault relationships, so that when measurements are taken, the system can quickly evaluate pre-established probability relationships rather than searching for fault assignments in real-time.
Solution Approach 2:
The patent transforms the diagnostic problem from determining absolute fault states to calculating probability distributions over fault hypotheses. By changing the parameter representation from binary fault/no-fault states to continuous probability values, the system maintains rich fault assignment information while using computationally efficient probabilistic updates based on measurements.
3Measurement precision
If extensive engineering information is incorporated into the diagnostic system, then diagnostic accuracy improves, but system complexity and data requirements increase
Solution Approach 1:
The system segments engineering knowledge into discrete probabilistic relationships within the Bayesian network, where each node and arc represents a specific piece of domain knowledge. This segmentation allows extensive engineering information to be incorporated in a structured, manageable way, with each knowledge element contributing locally to the overall diagnostic accuracy without creating monolithic system complexity.
Data Source
AI summary
A method and system for analyzing the cause of faults in a process engineering installation, wherein engineering information of the engineering installation, where the information contains information about the engineering installation components as well as their interconnection in the engineering installation, is provided in digital form in order to use the engineering information to create an inference model in the form of a probabilistic physical model of the engineering installation with probability distributions and prior variables, where measurement data from the engineering installation are used to perform Bayesian inference of fault probabilities during a diagnosis mode of the inference model.