Machinery Component Failure Modeling Using Controlled Damage Data
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Solution Overview
Problem
Current manufacturing systems face challenges in accurately generating training data for equipment failure prediction due to the difficulty in representing lifetime machine failure modes and data misrepresentation, leading to inefficient detection and management of equipment failures.
Innovation Solution
A system that induces controlled damage in manufacturing components to generate a lifetime failure dataset, using sensors to monitor and analyze progressive damage, and employs machine learning to create a usable lifetime model for predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If controlled damage is induced in components to generate lifetime failure datasets, then training data accuracy and representation of failure modes is improved, but device complexity and testing infrastructure requirements increase
Solution Approach 1:
The system performs preliminary actions by inducing controlled defects in components before actual failure occurs. This allows the collection of training data across the entire component lifecycle from healthy state through progressive degradation to failure, eliminating the need to wait for natural failures and providing comprehensive labeled datasets for machine learning models.
Solution Approach 2:
The system introduces an intermediary testing apparatus that acts as a mediator between the component and the failure state. This testing apparatus applies controlled stress and monitors the component throughout its lifecycle, enabling the collection of detailed degradation data without requiring the component to fail naturally in its operational environment.
2Measurement precision
If sensors monitor progressive damage from initial defect to failure, then detection precision and failure prediction accuracy is improved, but data management complexity and processing requirements increase
Solution Approach 1:
The system segments the component lifecycle into distinct phases (healthy, early degradation, advanced degradation, failure) and collects data specific to each phase. This segmentation allows for targeted analysis and modeling of different failure modes, making the complex data more manageable and interpretable for training predictive algorithms.
Solution Approach 2:
The system implements feedback mechanisms where sensor data from progressive damage monitoring is continuously analyzed and used to update the component's health status. This feedback loop enables real-time adjustment of monitoring parameters and provides continuous validation of predictive models, improving accuracy while managing data complexity through iterative refinement.
3Reliability
If machine learning models are trained on controlled failure data, then predictive maintenance capability is improved, but initial implementation cost and time investment increase
Solution Approach 1:
The system performs preliminary action by pre-collecting and labeling failure data during controlled testing phases before deployment. This pre-prepared labeled dataset eliminates the need for time-consuming data collection and labeling in the field, allowing machine learning models to be trained quickly and accurately when deployed for predictive maintenance.
Solution Approach 2:
The system creates copies of the controlled failure scenarios and datasets for training purposes. By replicating failure conditions in a controlled environment and storing the resulting datasets, the system can train multiple models and perform extensive validation without requiring additional physical failure events, significantly reducing implementation time.
Data Source
AI summary
A system of digitally representing equipment failure comprising: a testing assembly for testing a component of a manufacturing equipment wherein the component includes a defect in a critical area included in the component; a sensor in communications with the testing assembly for sensing a failure state of the component, a set of computer readable instructions adapted for: receiving a critical failure mode associated with the component, receiving a testing dataset from the sensor representing testing results produced by the testing assembly wherein the testing dataset includes initial data representing an undamaged component and a failure dataset representing a failed component, isolating a set of failure data representing a testing status of the component from initial testing to failure of the component determined by the critical failure mode, creating a usable lifetime model of the component according to the set of failure data.