Electro-Mechanical Failure Prediction Using Multi-Stress Degradation Models
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Solution Overview
Problem
Existing methods for predicting the life of electro-mechanical systems fail to accurately account for the combined effects of various stresses, such as mechanical, electrical, and process stresses, leading to inaccurate maintenance and potential failures.
Innovation Solution
A method for condition-based management that generates a stress profile and uses an accelerated degradation model, incorporating data from sensing units and simulating operations on a digital twin to predict failure instances and remaining life, with tuning based on actual failures using machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If condition-based maintenance techniques are used to predict life of electro-mechanical systems, then maintenance accuracy improves, but the combined effect of different types of stresses is not considered leading to inaccurate predictions
Solution Approach 1:
The patent combines multiple stress types (mechanical, electrical, thermal, process) into a unified stress profile that is fed into the accelerated degradation model. This merging of different stress factors allows the model to consider their combined effects on system degradation, resolving the contradiction between measurement precision and prediction reliability by ensuring comprehensive stress consideration.
Solution Approach 2:
The patent transforms operating conditions into standardized stress parameters and integrates them into the accelerated degradation model. By changing the representation of various stresses into comparable parameters that can be combined mathematically, the system achieves both accurate measurement of individual stresses and reliable prediction of their combined effects on system life.
2Productivity
If accelerated degradation models are used for predicting failure, then prediction capability improves, but the model requires tuning with actual failure data which may not be available
Solution Approach 1:
The patent performs preliminary calibration of the accelerated degradation model using accelerated life test data before actual field deployment. By pre-adjusting model parameters using controlled test data, the system reduces the need for extensive tuning with scarce actual failure data, maintaining prediction capability while reducing tuning complexity.
Solution Approach 2:
The patent implements a feedback mechanism where actual failure data and condition monitoring data are used to continuously refine and update the accelerated degradation model parameters. This feedback loop allows the model to improve its predictions over time while adapting to actual system behavior, balancing prediction capability with manageable tuning requirements.
Data Source
AI summary
Systems, devices, and methods of condition-based management of electro-mechanical systems are disclosed. The method includes generating a stress profile for the electro-mechanical system based on operating or simulating operation of the electro-mechanical system in accordance with a load profile, wherein the load profile indicative of operation duration and load capacity of the electro-mechanical system. The method further includes receiving condition data associated with the electro-mechanical system in operation from a plurality of sensing units and predicting a failure instance of the electro-mechanical system using an accelerated degradation model based on at least one of the stress profile and the condition data. The accelerated degradation model is generated when the electro-mechanical system is operated above a rated stress. The method further includes comparing the predicted failure instance with an actual failure instance upon failure of the electro-mechanical system, for tuning the accelerated degradation model.