Machine Impact Monitoring for Remaining Life Prediction
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Solution Overview
Problem
Existing systems face challenges in accurately predicting the remaining useful life (RUL) of industrial machines due to sensor limitations, calibration intricacies, and the need for synchronized data across diverse operational conditions, particularly neglecting swing and drop impacts which can cause significant structural damage.
Innovation Solution
A system that integrates sensors to collect impact data, processes it to assign damage values, and generates recommendations for maintenance using a RUL model to estimate machine life, accounting for swing and drop impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time sensor data is integrated into RUL calculations, then prediction accuracy is improved, but data quality and reliability challenges worsen due to sensor limitations and calibration intricacies
Solution Approach 1:
The patent introduces simulation data as an intermediary between sensor data and RUL calculations. The system generates simulated sensor data that mirrors real sensor characteristics and correlates it with known degradation patterns from physics-based models. This simulation layer mediates the relationship between imperfect real sensor data and RUL predictions, allowing the system to achieve accurate predictions while accounting for sensor limitations and calibration issues through the simulated reference framework
Solution Approach 2:
The system dynamically adjusts sensor parameters and calibration factors based on simulated reference data. By comparing real sensor readings with simulated equivalents under known conditions, the system automatically calibrates sensor parameters, compensates for drift, and adapts to changing sensor characteristics over time, thereby maintaining data quality and reliability despite sensor limitations
2Measurement precision
If sufficient operational data is accumulated to capture diverse usage patterns, then RUL determination accuracy is improved, but sampling time delay worsens
Solution Approach 1:
The system performs preliminary actions by pre-generating simulation data covering a wide range of operational conditions, usage patterns, and degradation scenarios before they occur in real operation. This pre-computed simulation database allows the system to immediately match real sensor data against relevant simulated scenarios without needing to accumulate extensive real-world data, thereby reducing sampling time delay while maintaining accurate RUL determination
Solution Approach 2:
The system creates copies of operational data through simulation. Instead of waiting to collect diverse real operational data over long periods, the system generates synthetic copies of sensor data that represent various usage patterns and environmental conditions. These simulated data copies can be immediately used for RUL prediction, eliminating the time delay associated with accumulating sufficient real operational data
3Measurement precision
If sophisticated modeling techniques are used to capture degradation processes, then prediction accuracy is improved, but system complexity worsens
Solution Approach 1:
The simulation environment serves as an intermediary that encapsulates sophisticated physics-based degradation models. Rather than directly implementing complex degradation models in the prediction system, the patent uses simulation to pre-compute degradation patterns under various conditions. This intermediary simulation layer handles the modeling complexity internally, allowing the actual RUL prediction system to use simpler comparison and matching algorithms while still achieving high accuracy through the simulated reference data
4Reliability
If synchronization between real-world data and simulated environments is ensured, then correlation reliability is improved, but technical challenge worsens due to synchronization requirements
Solution Approach 1:
The system replaces direct mechanical/temporal synchronization mechanisms with a data-driven correlation approach. Instead of requiring precise temporal alignment between real and simulated data streams through complex synchronization hardware and protocols, the patent uses simulation data that is inherently correlated with real operational patterns through physics-based models. The system matches real sensor data against simulated scenarios based on operational conditions rather than strict timing, substituting complex synchronization requirements with more manageable data correlation techniques
Data Source
AI summary
Systems and methods for predicting the conditions of one or more components of a machine are disclosed. The method includes receiving impact data from one or more sensors associated with the machine. The method includes processing the impact data to assign damage values to one or more components of the machine. The method includes generating one or more recommendations or machine life estimations based on the damage values in a user interface of the machine.