Sensor Fault Detection and Diagnosis for an Unmanned Quadrotor Helicopter

Zhong, Yujiang; Zhang, Wei; Zhang, Youmin; Zuo, Jiakuo; Zhan, Hanlin

doi:10.1007/s10846-019-01002-4

Cited by 34 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bias fault. This is one of the most common faults in sensors, corresponding to a constant offset added to the sensor output, which may be caused by inappropriate calibration or physical changes in the sensor [23]. Bias failures are a common fault in NPPs, and their maintenance can be costly [1].…”

Section: A Types Of Faultsmentioning

confidence: 99%

Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor

et al. 2022

View full text Add to dashboard Cite

Nuclear power plants (NPPs) are complex dynamic systems with multiple sensors and actuators. The presence of faults in the actuators and sensors can deteriorate the system's performance and cause serious safety issues. Although concerns about faults in the sensors and actuators in NPPs is a similarly important topic, only a few papers have discussed it. In this study, fault detection and diagnosis (FDD) based on neural networks (NN) and K-nearest neighbour (KNN) is addressed for a pressurized water reactor (PWR). Fault detection is first determined based on the NN. Second, the KNN algorithm is used to classify the faults. The proposed approach is capable of classifying a variety of actuator faults, sensor faults, and multiple simultaneous actuator and sensor faults. A set of simulation results is provided to demonstrate the accuracy of the FDD method. The classifier performance is further compared with other machine learning techniques.INDEX TERMS Fault classification, fault detection, K-nearest neighbor (KNN), neural networks (NNs), nuclear power plants (NPPs), pressurized water reactor (PWR).

show abstract

Section: A Types Of Faultsmentioning

confidence: 99%

Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For further evaluation of the two incorporated methodologies, The practicability diagnostic‐results of the proposed AFAA model showed that accuracy of the self‐ aware and efficiency of filtration for the deployment‐faults issued sensor via time‐varying samples accomplished up to 98.63% (data reliability is 98.63%), compared to the results of FDD scheme [7] that achieved about 90.4% through the time‐varying faults detection, and also results of the improved KAF [8] that reported 98% in the ability of fault diagnosis and filter its. Fig.…”

Section: Comparison With Related Workmentioning

confidence: 99%

Fault autonomous model handling through integrated adaptive‐filters for eliminating deployment faults in wireless sensor networks

Elsayed

El-Bakry

El-Sayed

2020

IET wirel. sens. syst.

View full text Add to dashboard Cite

“…In aerospace and several industrial applications, hardware redundancy-based techniques are the most common solutions to tackle faults on sensors. Such solutions require a set of redundant sensors to validate measurement data [ 22 ]. A typical approach in commercial and military aircraft is based on triple or more hardware redundancies to enhance safety and reliability [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

D’Amato

Nardi

Notaro

et al. 2021

Sensors

View full text Add to dashboard Cite

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

show abstract

Sensor Fault Detection and Diagnosis for an Unmanned Quadrotor Helicopter

Cited by 34 publications

References 23 publications

Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor

Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor

Fault autonomous model handling through integrated adaptive‐filters for eliminating deployment faults in wireless sensor networks

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

Contact Info

Product

Resources

About