Cuestionario vinculado a una guía · Medio Nivel
Prueba de IA en ferrocarriles
Explore el monitoreo del estado de los ferrocarriles, la inspección de imágenes, la automatización operativa, las limitaciones de energía, los gemelos digitales y los límites de las alertas de mantenimiento.
Pregunta 1 de 8
¿Cuál es el principal beneficio del mantenimiento predictivo en los ferrocarriles?
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Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.
- ¿Cuál es el principal beneficio del mantenimiento predictivo en los ferrocarriles?
- Hace que los trenes viajen más rápido que su velocidad nominal.
- Señala los componentes que probablemente fallen antes de que provoquen averías o accidentes.
- Elimina la necesidad de pistas físicas.
- Sustituye los billetes de tren por reconocimiento facial
- Which approach can examine railway camera images for visible track or lineside defects?
- Text-to-speech synthesis without image input
- An audio-only voiceprint matcher
- A language translation system with no visual processing
- Computer vision trained or configured to recognize relevant visual features
- What functions can Automatic Train Operation perform in the railway operational framework?
- Only printing passenger tickets
- Only predicting the weather without controlling motion
- Automated traction, braking, speed regulation, and stopping functions
- Only renaming maintenance image files
- How should an energy-efficient railway speed profile relate to operational requirements?
- Optimize energy use while satisfying applicable journey, capacity, and speed constraints
- Ignore stopping and speed requirements whenever energy use falls
- Guarantee a fixed percentage saving on every route
- Stop all trains indefinitely to claim perfect efficiency
- What does a railway digital twin represent?
- A second human driver with the same appearance
- A replacement for the physical track made only of software
- A virtual representation of physical rail assets or infrastructure
- A guarantee that every simulated result occurs in the real network
- Does the presence of ATO establish that a railway uses machine learning to drive its trains?
- Yes; every automated control system is necessarily a neural network
- No; ATO specifies automated functions, while the implementation must be examined to identify any learned model
- Yes; all ATO controllers are trained on social-media video
- No; ATO has no connection with train motion
- Why should a railway maintenance team assess an AI fault alert before deciding on an intervention?
- An alert can be mistaken and must be considered with engineering evidence and operational consequences
- Every alert proves that a component will fail at a known time
- A model score eliminates the need for asset information
- Ignoring all alerts is always more accurate than reviewing them
- In the ERA ATO principles, what limits an energy-efficient speed profile besides journey timing requirements?
- Only the color of the train's exterior
- Only the length of its passenger announcements
- No other speed constraint once the energy objective is set
- The ETCS most restrictive speed profile