Component And Unit Malfunction Prediction Using Markov Chains
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Solution Overview
Problem
Existing methods for predicting component malfunctions in complex systems are inefficient, require complex reliability models specific to each machine type, and fail to predict correlated random events or malfunctions across multiple parameters, leading to high computational costs and limited predictive horizons.
Innovation Solution
A method using conditional probability distributions to predict malfunctions based on current parameter values, combining Markov models with transition matrices to determine future probabilities of malfunctions, applicable to various component types without requiring specific physical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reliability models are developed for each specific machine type to predict malfunctions, then prediction accuracy for that machine type is improved, but model complexity and computational requirements increase significantly
Solution Approach 1:
The patent applies universality by creating a single generic malfunction prediction model that can be applied to multiple different machine types (gas turbines, steam turbines, compressors, pumps, etc.). Instead of developing separate reliability models for each machine type, the invention uses a unified approach with standardized parameters and probability distributions that work across diverse equipment, thereby reducing model complexity while maintaining prediction capability.
Solution Approach 2:
The patent transforms specific machine parameters into standardized probability distributions. By converting physical parameters into probabilistic representations and using generic probability distribution functions, the model can handle different machine types through parameter adjustments rather than requiring completely different model structures, thus reducing overall system complexity.
2Measurement precision
If machine-specific reliability models are created for each component type, then prediction precision for that component is improved, but the overall system becomes computationally inefficient
Solution Approach 1:
The invention creates a universal prediction framework that handles multiple component types through a single computational model. The generic model uses standardized probability distributions and can process predictions for gas turbines, steam turbines, compressors, pumps and other equipment using the same computational pathway, significantly improving computational efficiency compared to maintaining separate models for each component type.
3Reliability
If new reliability models are created for each component type to improve prediction accuracy, then prediction quality is improved, but model creation effort and costs increase
Solution Approach 1:
The patent establishes a universal model creation process that can be applied to any component type without requiring new model development. The generic framework with standardized probability distributions and prediction algorithms allows the same model structure to be deployed across different equipment types, eliminating the need for repeated model creation efforts and reducing associated costs.
Data Source
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AI summary
Method for predicting a malfunction of a component of a unit comprising the steps of: providing a transition matrix of a parameter of the component, wherein the transition matrix comprises for a number of discrete value states of said parameter the probabilities to switch from one discrete value state to another within a certain time period; providing the conditional probability distribution for the malfunction given the discrete value states of said parameter; providing a current discrete value state of said parameter; determining a conditional probability distribution of the discrete value states of said parameter given the current discrete value state of the parameter for a future point in time based on the current discrete value state of the parameter and on the transition matrix by the use of a Markov chain; and determining a probability for the malfunction for the future point in time based on the conditional probability distribution of the discrete value states of said parameter for the future point in time and on the conditional probability distribution for the malfunction given the discrete value states of said parameter.