Home/Science & Research/Autonomous Underwater Vehicle Fault and Performance Degradation State Simulation Dataset
Autonomous Underwater Vehicle Fault and Performance Degradation State Simulation Dataset
#Abnormal state representation #State transition prediction #Fault impact analysis #Recovery state modeling #Underwater vehicle health monitoring #Fault-tolerant control research #Fault recovery evaluation #Simulation system validation
- 8000
- 0.5GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-03
AI Analysis & Value Prop
This dataset simulates autonomous underwater vehicle behavior under faults such as reduced thruster efficiency, control surface anomalies, sensor bias, communication loss, and partial actuator failure. It covers fault conditions, state observations including pose and velocity, control and recovery logs, and information on fault severity, affected states, control response, and recovery status. The data supports world model training for abnormal-state representation and state-transition learning, as well as fault analysis, fault-tolerant control, and recovery strategy evaluation.
Dataset Insights
Sample Examples
{}
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| fault_condition | object | Original conditions for injected simulation faults, including fault type, location, start time, and severity. |
| recovery_status | object | Derived recovery progress, recovery outcome, and state transition labels based on recovery logs and state changes. |
| state_observation | object | Original state observations collected under fault conditions, including vehicle pose, velocity, and sensor bias. |
| control_recovery_log | object | Original logs of control commands, controller modes, communication status, and recovery actions during faults. |
| fault_severity_score | number | Composite severity score calculated from fault magnitude, actuator failure proportion, and impact on system state. |
| affected_state_summary | object | Derived summary of the main affected states and deviation metrics, based on the original fault conditions and state observations. |
| control_response_metrics | object | Metrics for control tracking and dynamic response, calculated from control commands and state changes. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- Which fault scenarios are included?
- The simulations cover reduced thruster efficiency, control surface anomalies, sensor bias, communication loss, and partial actuator failure. Fault conditions include details such as fault type, location, start time, and severity.
- What state and control information does the dataset contain?
- Fields cover state observations such as pose, velocity, and sensor bias, along with control commands, controller mode, communication status, and recovery actions. The dataset also includes fault severity, affected states, control response, and recovery status.
- Which training and evaluation tasks does it support?
- It can support abnormal-state representation and state-transition learning for world models, as well as fault impact analysis, control response evaluation, and recovery state modeling. Since the data is simulated, validate it against real-world measurements before applying it to physical systems.
- How can I access the data, and what licensing applies?
- Check the product page or contact the data provider for current access options, delivery specifications, and licensing terms. Before use, confirm that the license covers your intended activities, such as training, evaluation, or commercial deployment.
Can't find the data you need?
Post a request and let data providers reach out to you.
Get this Dataset
Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Autonomous Underwater Vehicle Fault and Performance Degradation State Simulation Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/177ef98a94fbae40e4cb014580345a17?dataset_scene_id=20},
urldate={2026-10-28},
keywords={autonomous underwater vehicle dataset,underwater vehicle fault simulation,robot performance degradation data,underwater vehicle state observations,fault recovery logs,world model training data,underwater vehicle fault-tolerant control,state transition modeling},
version={1.0}
}Using this in research? Please cite us.
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder