Component Malfunction Prediction Using Conditional State Transitions
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Solution Overview
Problem
Existing methods for predicting malfunctions in industrial assets and fleets are complex, computationally inefficient, and require separate reliability models for each component type, making them unsuitable for predicting malfunctions over an extended future time horizon.
Innovation Solution
A method that determines a conditional probability distribution for a component parameter at a future time point given its current value, and uses this distribution to calculate the probability of a malfunction, allowing for predictions independent of component types and scalable across components, units, and fleets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate reliability models are created for each component type, then prediction accuracy for specific components is improved, but model complexity and computational efficiency deteriorate
Solution Approach 1:
The patent applies universality by creating a single probabilistic prediction framework that can handle multiple component types (gas turbines, current transformers, etc.) and various parameter distributions (normal, log-normal, exponential, Weibull) through a unified approach. Instead of developing separate reliability models for each component type, the invention uses a general probabilistic model that adapts to different component characteristics through parameter specification, thereby reducing overall system complexity while maintaining prediction accuracy.
2Measurement precision
If separate reliability models are created for each component type, then component-specific prediction accuracy is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent achieves computational efficiency through universality by implementing a single probabilistic prediction engine that can process multiple component types simultaneously. The framework uses standardized probability distribution functions and a unified prediction algorithm that adapts to different components through parameter input rather than structural modification, significantly reducing computational overhead compared to maintaining separate models for each component type.
3Measurement precision
If component parameters are continuously monitored and analyzed, then malfunction prediction accuracy is improved, but data processing complexity and computational resources increase
Solution Approach 1:
The patent applies parameter changes by transforming raw component parameter data into probability distribution parameters (mean, standard deviation, shape parameters) that feed into the probabilistic prediction model. Instead of directly analyzing complex time-series data, the invention converts monitoring data into standardized statistical parameters, thereby simplifying the data processing pipeline while maintaining prediction accuracy through the use of established probability distribution functions.
Data Source
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AI summary
Method for configuring an apparatus or method for prediction of a malfunction of a unit, comprising the steps of: providing components of the unit; providing single malfunctions for each component; providing probabilities for the single malfunctions of each component; providing at least one parameter of each component for each single malfunction of this component; dividing each parameter into a plurality of discrete value states; providing values of the parameters; determining a transition matrix for each parameter on the basis of the measurements of this parameter, wherein the transition matrix comprises for the plurality of discrete value states of each parameter the probabilities to switch from one of the discrete value states to another of the discrete value states within a certain time period; determining the probabilities of the discrete value states of the parameters on the basis of the measurements of the parameters; and providing the conditional probabilities of the discrete value states of the parameters given the corresponding single malfunction.