Equipment Failure Probability Modeling for Real-Time Life Estimation
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Solution Overview
Problem
Conventional equipment health monitoring methods for mechanical damage are inaccurate due to assumptions about future production parameters and material non-linearity, leading to uncertainties in predicting equipment failure and lifespan.
Innovation Solution
A real-time prediction method using acoustic emission data, finite element analysis, and Polynomial Chaos Expansion (PCE) to model crack extension, enabling accurate prediction of equipment failure probability and remaining lifespan by correlating AE signal characteristics with crack behavior and material properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional non-destructive testing inspection during plant shutdown is used, then equipment health monitoring is performed, but prediction accuracy is poor due to assumptions about future production parameters and material non-linearity
Solution Approach 1:
The system performs preliminary action by continuously monitoring acoustic emission signals and calculating probability of failure in real-time during equipment operation, rather than waiting for shutdown inspections. This allows early detection of crack initiation and growth, enabling proactive maintenance decisions before actual failure occurs, thereby improving prediction accuracy and reducing uncertainty.
Solution Approach 2:
The patent replaces conventional mechanical inspection methods (shutdown-based non-destructive testing) with an acoustic emission-based monitoring system that detects crack-related acoustic signals in real-time. This substitution enables continuous monitoring without plant shutdown, capturing actual operational conditions and material non-linearity effects, thus improving prediction reliability.
2Measurement precision
If real-time acoustic emission monitoring and Polynomial Chaos Expansion are implemented, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces Polynomial Chaos Expansion (PCE) as an intermediary computational model that bridges the complex acoustic emission data and finite element analysis with the probability of failure calculation. The PCE provides a efficient surrogate model that captures non-linear material behavior and parameter variations without requiring full-scale complex simulations in real-time, thus improving accuracy while managing system complexity.
Solution Approach 2:
The system changes parameters by transitioning from deterministic inspection data to probabilistic failure prediction using acoustic emission signals. It incorporates real-time operating parameters (temperature, pressure, production rate) and material properties as random variables in the PCE framework, enabling accurate prediction under varying operational conditions while maintaining computational efficiency.
3Productivity
If continuous real-time monitoring is implemented, then maintenance optimization improves, but data processing requirements and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-calculating probability of failure values and remaining life predictions using the PCE model based on current acoustic emission signals and operating conditions. This allows maintenance decisions to be made proactively based on predicted failure probabilities, optimizing maintenance scheduling and resource allocation without requiring intensive real-time computational resources during critical decision-making moments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables proactive maintenance strategies, reduces the risk of incidents like Loss of Primary Containment (LOPC), optimizes maintenance resources, and provides accurate life assessment of equipment.
Implementation Method 1
receiving acoustic emission parameters corresponding to the equipment component and determining a correlation between the acoustic emission parameters and strain to deduce a level of damage of the equipment component
Data Source
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AI summary
Methods and systems for calculating a probability of failure and / or estimating a lifetime of an equipment component are disclosure. In an embodiment, a method of calculating a probability of failure of an equipment component comprises: generating a finite element model of the equipment component using device properties of the equipment component; using the finite element model of the equipment component to construct a polynomial basis for a polynomial chaos expansion; calculating expansion coefficients for the polynomial chaos expansion which express creep stress and strain in the equipment component as a function of operating parameters of the equipment component; receiving measured operating parameter values for the equipment component; and calculating a probability of failure of the equipment component using the measured operating parameter values.