Train Component Failure Prediction via Virtual Modeling
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Solution Overview
Problem
Current systems for remotely controlling train components do not effectively predict failures and implement repair or replacement protocols ahead of time, leading to potential downtime and increased maintenance costs.
Innovation Solution
A system that includes a data acquisition hub and a virtual system modeling engine to acquire real-time and historical data from sensors, simulate in-train forces using physics-based equations, and predict component failures, allowing for proactive implementation of repair, replacement, or operational protocols at a repair facility before the predicted failure time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time and historical data are acquired and analyzed using machine learning to predict component failures, then maintenance timing and reliability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing real-time and historical sensor data to predict component failures before they occur. The machine learning models process data from multiple sensors to identify patterns and trends that indicate impending failures, enabling maintenance to be scheduled in advance rather than reacting to actual failures.
Solution Approach 2:
The patent introduces an intermediary layer between raw sensor data and maintenance decisions. Machine learning models serve as intermediaries that process, analyze, and interpret sensor data from multiple sources, transforming raw data into actionable failure predictions. This intermediary layer manages the complexity by encapsulating the data processing logic within trained models.
2Measurement precision
If machine learning models are trained using extensive real-time and historical contextual data from multiple trains, then prediction accuracy is improved, but data acquisition and processing time increase
Solution Approach 1:
The system performs preliminary data collection and model training in advance. Historical contextual data from multiple trains is collected and used to train machine learning models before they are deployed for real-time prediction. This preliminary training phase enables the models to be ready for rapid inference when deployed, reducing real-time processing requirements.
Solution Approach 2:
The machine learning models are trained on universal data from multiple trains with similar locomotive types, making the models applicable across different trains and contexts. This multi-functional training approach allows a single model to serve multiple trains, improving prediction precision through larger datasets while avoiding the need to build separate models for each train.
3Productivity
If proactive maintenance protocols are implemented based on predicted failure times, then downtime and maintenance costs are reduced, but the system requires higher automation and monitoring capabilities
Solution Approach 1:
The system enables self-service by allowing the train maintenance system to automatically monitor its own component health and schedule maintenance based on predicted failures. The machine learning models continuously assess component conditions and generate maintenance recommendations without requiring constant human intervention, enabling the system to manage its own maintenance needs.
Solution Approach 2:
The system implements feedback loops where sensor data from train operations is continuously fed into machine learning models, which generate failure predictions that trigger maintenance protocols. The outcomes of maintenance actions are also fed back into the system to improve future predictions, creating a closed-loop feedback mechanism that automatically adjusts and optimizes maintenance scheduling.
Data Source
AI summary
A system may include a data acquisition hub connected to databases and sensors associated with locomotives, systems, or components of a train and configured to acquire real-time and historical configuration, structural, and operational data in association with inputs derived from real time and historical contextual data relating to a plurality of trains. The system may include a virtual system modeling engine configured to receive results of a non-destructive evaluation of a train component, simulate in-train forces, determine a predicted time of failure for the train component based on an evaluation of stresses that have already been applied to the component and expected future stresses, and implement repair, replacement, or operational protocols for the train component before or at a repair facility that will be reached by the train ahead of a predetermined minimum threshold time period before the predicted time of failure.