Fault Detection and Identification on UAV System with CITFA Algorithm Based on Deep Learning

Olyaei, Mohammad Hasan; Jalali, Hasan; Noori, Amin; Eghbal, Najmeh

doi:10.1109/icee.2018.8472529

Cited by 17 publications

(11 citation statements)

References 11 publications

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“…The authors of [42] present a fault detection and identification method for sensors and actuators on a fixed-wind vehicle, based on deep learning. For faults classification, they introduced an algorithm called Color Images obtained from Time-Frequency-Amplitude (CITFA) while the simulations give accuracy of 98%.…”

Section: Sensors Fault Diagnosismentioning

confidence: 99%

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

Fourlas

Karras

2021

Machines

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The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. This has been also reflected in the technology of Unmanned Aerial Vehicles (UAVs), where the growing demand for more reliable performance necessitates the development of sophisticated techniques that provide fault diagnosis and fault tolerance in a timely and accurate manner. Typically, a UAV consists of three types of subsystems: actuators, main structure and sensors. Therefore, a fault-monitoring system must be specifically designed to supervise and debug each of these subsystems, so that any faults can be addressed before they lead to disastrous consequences. In this survey article, we provide a detailed overview of recent advances and studies regarding fault diagnosis, Fault-Tolerant Control (FTC) and anomaly detection for UAVs. Concerning fault diagnosis, our interest is mainly focused on sensors and actuators, as these subsystems are mostly prone to faults, while their healthy operation usually ensures the smooth and reliable performance of the aerial vehicle.

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Section: Sensors Fault Diagnosismentioning

confidence: 99%

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

Fourlas

Karras

2021

Machines

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“…Bing Feng Ng and Kin Huat Low are with the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798 (e-mail: bingfeng@ntu.edu.sg; mkhlow@ntu.edu.sg), *Kin Huat Low, corresponding author (email: mkhlow@ntu.edu.sg) Using Colour Images obtained from Time-Frequency-Amplitude (CITFA) graphs, one study proposes generating a database that contains actuator and sensor data corresponding to faultless and faulty scenarios. The data in this database is then used to train deep neural networks and, thus, perform fault diagnosis [18]. Recently, researchers studied neural networks with fuzzy logic systems, allowing them to benefit from superior learning capabilities and inference capabilities, respectively [19], giving rise to neuro-fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Hardware-in-the-loop Simulation for Quadrotor Fault Diagnosis Enhancing Airworthiness using OS-Fuzzy-ELM

Thanaraj

Sai

et al. 2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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With the rising adoption of multi-rotor UAVs, it has become ever more crucial that their airworthiness is ensured, especially for hobby-grade UAVs. For some UAVs, their onboard components might not be as reliable as required and as such, faults can occur during flight operations and may develop to cause catastrophe. Hence, these faults need to be accurately diagnosed and quickly mitigated. This paper presents a fault diagnosis model for a quadrotor subjected to partial actuator faults. Flight simulations, with actuator and GPS sensor fault injections, are performed on a hardware-in-the-loop experimental setup to gather flight data consisting of multiple sensors. Based on this data, a preliminary controllability threshold analysis is conducted for the quadrotor. After that, a fault diagnosis model using an online sequential fuzzy-extreme learning machine (OS-Fuzzy-ELM) is trained to locate the actuator faults on the quadrotor UAV. The trained model presents an average testing accuracy and macro-averaged F1 score of 80.2% and 78.0%. A subsequent study to isolate sensor and actuator faults presents the testing accuracy and macroaveraged F1 score to be 1.95% and 1.21%, marginally better than a fault diagnosis model based on a single-layer feedforward network

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“…Chen et al [9] realized the fault diagnosis of UAV sensor based on the back propagation (BP) neural network optimized by genetic algorithm. Olyaei et al [10] researched a variety of sensor and actuator faults of UAV based on time-frequency-amplitude graphs and deep neural network. Meanwhile, in the field of fault classification and fault pattern recognition, a large number of typical neural networks have also emerged.…”

Section: Introductionmentioning

confidence: 99%

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

Che

2021

IEEE Access

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If the fault sources can be located in a timely and accurate manner when an unmanned aerial vehicle (UAV) has an early minor fault, a major accident can be avoided. In light of the early fault of the six-rotor UAV motors, this paper proposes a fusion fault diagnosis model based on Conformal Fourier Transform (CFT) and the improved Self-Organizing Feature Map (SOM) neural network. Firstly, to overcome the weakening of UAV fault information caused by external interference and solve the problem that the non-uniform distribution of the collected flight data affects the accuracy of the frequency domain processing, on the basis of changing the processing method of the Fourier integral kernel and using high-order numerical integration, an improved Conformal Fourier Transform (CFT) algorithm optimized by spectrum refinement strategy is proposed. Then this algorithm is used to further intensify the main features and build a clear fault state in a cycle processing method. Secondly, an improved SOM neural network is designed. We use the unweighted average distance (UPGMA) of the agglomerative hierarchical clustering algorithm to detect the clustering areas of each pattern category of the data samples, and the weight vectors of different clustering areas are used to randomly initialize the connection weights of SOM network, thereby improving the clustering speed and convergence effect. On this basis, CFT algorithm is integrated into the improved SOM network to realize the multi-module collaboration strategy and finally form a new fusion fault diagnosis model. INDEX TERMS CFT algorithm, improved SOM network, main feature intensification, hierarchical clustering, fault diagnosis.

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Fault Detection and Identification on UAV System with CITFA Algorithm Based on Deep Learning

Cited by 17 publications

References 11 publications

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

Hardware-in-the-loop Simulation for Quadrotor Fault Diagnosis Enhancing Airworthiness using OS-Fuzzy-ELM

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

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