Intelligent Vehicle Embedded Sensors Fault Detection and Isolation Using Analytical Redundancy and Nonlinear Transformations
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Summary
The proposed method uses analytical redundancy and a nonlinear transformation to generate the residual value allowing the fault detection, and a strategy dedicated to the optimization of the detection parameters choice is developed.
- Type
- article
- Published
- 2017-01-01
- Cited by
- 22
- References
- 31
- Access
- Open access
- OpenAlex
- https://openalex.org/W2578404781
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:54760953
Keywords
Redundancy (engineering), Fault detection and isolation, Residual, Nonlinear system, Engineering
References
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- Fault diagnosis in dynamic systems: theory and application
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- Design of a Fault Detection and Isolation System for Intelligent Vehicle Navigation System
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- Robust fault diagnosis of stochastic systems with unknown disturbances
- A literature review on Fault Diagnosis methods for manned and unmanned helicopters
- Evaluation of distribution fault diagnosis algorithms using ROC curves
- Multi-sensor localization - Visual Odometry as a low cost proprioceptive sensor
- Detection of Sensor Faults in Small Helicopter UAVs Using Observer/Kalman Filter Identification
- Robust fault detection of dynamic systems via genetic algorithms
- Sensor and actuator fault detection in small autonomous helicopters
- A new approach to robust fault detection and identification
- Robust fault detection using eigenstructure assignment: a tutorial consideration and some new results
- Sensor fault detection and identification in a mobile robot
- A GNSS Based Slide and Slip Detection Method for Train Positioning
- Fault detection and identification in a mobile robot using multiple-model estimation
Cited by
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- Autonomous vehicle perception: The technology of today and tomorrow
- Research on Customer Marketing Acceptance for Future Automatic Driving—A Case Study in China City
- Real-Time Sensor Anomaly Detection and Identification in Automated Vehicles
- Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications
- Sensor Fault Detection and Signal Restoration in Intelligent Vehicles
- A Data-Based Approach for Sensor Fault Detection and Diagnosis of Electro-Pneumatic Brake
- Are “Hard Sciences” Enough for Teaching Applied Control Design? Driving Assistance and Autonomy in Vehicles as a Case Study.
- Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors
- Artificial intelligence applications in the development of autonomous vehicles: a survey
- A Fault Detection and Diagnosis System for Autonomous Vehicles Based on Hybrid Approaches
- Robust Path Tracking Control for Autonomous Vehicle Based on a Novel Fault Tolerant Adaptive Model Predictive Control Algorithm
- A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM
- Conceptual design of a trust model for perceptual sensor data of autonomous vehicles
- Five key components based risk indicators ontology for the modelling and identification of critical interaction between human driven and automated vehicles
- Autonomous Driving Security: State of the Art and Challenges
- Otonom Taşıyıcı Araçlardaki Hataların Teşhisi için Evrişimli Sinir Ağları Tabanlı Çoklu Heterojen Sensörlerin Füzyon Yöntemi
- Onboard Sensors-Based Self-Localization for Autonomous Vehicle With Hierarchical Map
- Infrastructure-Enabled GPS Spoofing Detection and Correction
- Generative Abnormal Data Detection for Enhancing Cellular Vehicle-to-Everything-Based Road Safety
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