Crowd-Sourced Maintenance System for Transportation Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional maintenance systems for transportation vehicles are inefficient, relying on manual reporting by crew members and are prone to errors due to reliance on memory, which increases workload and delays in addressing passenger-reported issues.
Innovation Solution
A crowd-sourced maintenance system using machine learning that aggregates passenger input to develop a training dataset, providing predictive, preventive, or prescriptive responses to address problems, such as alerting crew members to reset Wi-Fi or notify passengers of available solutions like blankets for temperature issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual reporting by crew members is used, then the system is simple to operate, but the workload increases and errors occur due to reliance on memory
Solution Approach 1:
The system enables passengers to self-report maintenance issues directly through their PEDs or seat devices, eliminating the need for crew members to manually record problems. The automated system captures problem details directly from the source, reducing crew workload while improving accuracy and efficiency of maintenance reporting
Solution Approach 2:
The patent replaces the manual mechanical process of crew members remembering and writing down problems with an automated electronic system. The machine learning model and database automatically process, store, and analyze maintenance data, substituting human memory and manual recording with computational systems that improve both efficiency and accuracy
2Device complexity
If manual entry of problem details is required, then the system complexity is low, but time is lost due to waiting for crew response
Solution Approach 1:
The system performs preliminary actions by automatically capturing, storing, and analyzing maintenance data as it is reported. The machine learning model pre-processes the information and can immediately provide diagnostic suggestions or alert relevant personnel, eliminating the time delay associated with manual data entry and response coordination
Solution Approach 2:
The system implements automated feedback loops where maintenance data is immediately processed and analyzed. The machine learning model provides real-time or near-real-time feedback on reported issues, enabling faster response times while the automated database management handles data storage and retrieval without manual intervention
3Ease of manufacture
If crew members must remember problem details, then the system is easy to implement, but reliability decreases due to memory-dependent accuracy
Solution Approach 1:
The system creates accurate digital copies of maintenance data directly from the source (passenger reports, sensor data). Instead of relying on human memory, the automated system captures and stores precise problem details, device identifiers, and contextual information in a database, ensuring reliable and accurate record-keeping that can be consistently retrieved and analyzed
Data Source
AI summary
Methods and systems are provided for communicating an announcement to passengers on a transportation vehicle. One method includes receiving by a first device, an input from a passenger of a transportation vehicle, the input reporting a problem at the transportation vehicle; transmitting the input by the first device to a second device; evaluating by the second device whether the problem identified by the input is to be resolved at the transportation vehicle; generating a response by the second device to address the problem, when the evaluation indicates that the problem can be resolved at the transportation vehicle; and updating a data structure by the second device for addressing the problem after the transportation vehicle has reached a destination, when the evaluation indicates that the problem cannot be resolved at the transportation vehicle.