Train Asset Health Visualization via Color-Coded GUI Flagging
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Solution Overview
Problem
Current systems for monitoring the health of train assets are cumbersome and difficult to interpret, making it challenging for operators to efficiently and accurately evaluate the health of train assets in real-time, and do not provide timely alerts for potential issues.
Innovation Solution
A system comprising a sensing unit with sensors to generate health data, a controller to display this data in a graphical user interface (GUI), allowing selection and visualization of assets and sub-assets, and flagging of sub-assets based on health data for immediate action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current systems for receiving and interpreting status alerts are used, then health data can be collected, but the system becomes cumbersome and difficult to interpret
Solution Approach 1:
The patent employs color-coded indicators in the graphical user interface to represent different health statuses of train assets. Healthy assets are displayed in green, warning states in yellow, and critical states in red, enabling operators to quickly interpret system health without complex data processing
Solution Approach 2:
The system segments the train into distinct assets (locomotive, wagons, brakes, engine, fuel system) and displays each as separate selectable items in the GUI. This segmentation allows operators to focus on specific assets without being overwhelmed by comprehensive system data, reducing operational complexity
2Measurement precision
If comprehensive health monitoring is implemented, then accurate health evaluation is achieved, but the system complexity increases
Solution Approach 1:
The monitoring system is divided into separate sensor units for different asset types, with each sensor independently collecting and transmitting data to the controller. This modular segmentation achieves comprehensive health monitoring while keeping individual system components simple and manageable
Solution Approach 2:
The controller acts as an intermediary between the sensor units and the graphical user interface. It receives raw sensor data, processes and interprets health status, and presents simplified visual representations to operators, thereby achieving accurate health evaluation without exposing operators to system complexity
3Loss of time
If real-time health data collection is implemented, then timely maintenance actions are enabled, but the device complexity increases
Solution Approach 1:
Sensor units continuously monitor asset health parameters and transmit data to the controller without interruption. This continuous data collection enables real-time detection of deteriorating conditions, allowing maintenance actions to be taken immediately when thresholds are breached, minimizing time loss
Solution Approach 2:
The system automatically flagging critical assets in the graphical user interface without requiring operator intervention. The controller autonomously processes sensor data, identifies anomalies, and highlights them in the GUI, enabling timely maintenance decisions while reducing operational complexity
Data Source
AI summary
A system for analyzing health of assets in train is provided. The system includes a sensing unit configured to generate health data of sub-assets. The system also includes a controller in communication with the sensing unit to receive the health data. The controller is configured to display, in a graphical user interface (GUI), a list of assets configurable to allow selection of one asset therefrom. The controller is configured to receive an input command, in the GUI, to select a desired asset from the list of assets for requesting information about health of the desired asset. The controller is configured to display, in the GUI, a visual representation of the desired asset, along with the sub-assets, in response to the input command. The controller is configured to flag, in the GUI, the sub-assets in the visual representation of the desired asset based on the health data of the sub-assets.