scholarly journals EKF-Based Actuator Fault Detection and Diagnosis Method for Tilt-Rotor Unmanned Aerial Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiaxin Gao ◽  
Qian Zhang ◽  
Jiyang Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Fault Detection And Diagnosis ◽  
Actuator Fault ◽  
Tilt Rotor ◽  
Aerial Vehicles ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
Detection And Diagnosis

Flight safety is of vital importance for tilt-rotor unmanned aerial vehicles (UAVs), which can take off and land vertically as well as cruise at high speed, especially in different kinds of complex environment. As being the executor of the flight control, the actuator failure will directly affect the controllability of the tilt-rotor UAV, and it has high probability of causing fatal personal injury and financial loss. However, due to the limitation of weight and cost, small UAVs cannot be equipped with redundant actuators. Therefore, there is an urgent need of fault detection and diagnosis method for the actuators. In this paper, an actuator fault detection and diagnosis (FDD) method based on the extended Kalman filter (EKF) and multiple-model adaptive estimation (MMAE) is proposed. The actuator deflections are added to the state vector and estimated using EKF. The fault diagnosis algorithm of MMAE could assign a conditional probability to each faulty actuator according to the residual of EKF and diagnose the fault. This paper is structured as follows: first, the structure and model of tilt-rotor UAV actuator are established. Then, EKF observers are introduced to estimate the state vector and to calculate residual sequences caused by different faulty actuators. The residuals from EKFs are used by fault diagnosis algorithm to assign a conditional probability to each failure condition, and fault type can be diagnosed according to the probabilities. The FDD method is verified by simulations, and the results demonstrate that the FDD algorithm could accurately and efficiently diagnose actuator fault without any additional sensor.

Method on the Fault Detection and Diagnosis for the Railway Turnout Based on the Current Curve of Switch Machine

2013 ◽  
Vol 427-429 ◽  
pp. 1022-1027 ◽  
Author(s):  
Xue Mei Mo ◽  
Yu Fang ◽  
Yun Guo Yang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Location ◽  
Fault Detection And Diagnosis ◽  
Curve Matching ◽  
Reference Curve ◽  
Current Curve ◽  
Diagnosis Method ◽  
Detection And Diagnosis ◽  
Railway Turnout

This paper proposes a method of the fault detection and diagnosis for the railway turnout based on the current curve of switch machine. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted in the paper. Based on envelope and morpheme match algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Moreover, the SVM-based fault diagnosis method is used to make sure that the fault conditions could be diagnosed intelligently. Finally, the experimental results show that the proposed method can accurately identify the turnout fault status in the conversion process, and the accuracy rate in the diagnosis of the fault location is above 98%, which verify the effectiveness of the method in the fault detection and diagnosis.

Development on Fault Detection and Diagnosis of Unmanned Aerial Vehicles

2014 ◽  
Vol 494-495 ◽  
pp. 861-864
Author(s):  
Yi Peng Zhang ◽  
Ke Cai Cao
Keyword(s):  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
State Of The Art ◽  
Fault Detection And Diagnosis ◽  
The Past ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Detection And Diagnosis

The reliability of unmanned aerial vehicles (UAVs) has caught the attention of many researchers in the past decades. This paper presents a review on the development and important issues of state-of-the-art researches in the field of fault detection and diagnosis (FDD) techniques. Faults on an individual unmanned aerial vehicle or a group of unmanned aerial vehicles are considered for providing an overall picture of fault detection and diagnosis approaches.

Method on Fault Detection and Diagnosis for Track Circuit Based on Main Rail Voltage

2014 ◽  
Vol 670-671 ◽  
pp. 1172-1178
Author(s):  
Da Zhuang Wu ◽  
Yu Fang ◽  
Quan Song Ma
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Location ◽  
Fault Detection And Diagnosis ◽  
Fault Classification ◽  
Curve Matching ◽  
Reference Curve ◽  
Diagnosis Method ◽  
Track Circuit ◽  
Detection And Diagnosis

This paper proposes a method of the fault detection and diagnosis for the railway circuit of ZPW-2000 system based on the main track voltage curve. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted. Based on envelope algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Then, the SVM-based fault diagnosis method is used to make sure that the fault classification could be diagnosed intelligently. The experiment results show that the proposed method can accurately identify the track circuit fault state, and the accuracy rate in the diagnosis of the fault location is above 99%, which verify the effectiveness of the method in the fault detection and diagnosis.

Design of a fault detection and diagnose system for intelligent unmanned aerial vehicle navigation system

2018 ◽  
Vol 233 (6) ◽  
pp. 2170-2176 ◽  
Author(s):  
Qian Zhang ◽  
Xueyun Wang ◽  
Xiao Xiao ◽  
Chaoying Pei
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Fuzzy System ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
Aerial Vehicles ◽  
Detection And Diagnosis

A secure control system is of great importance for unmanned aerial vehicles, especially in the condition of fault data injection. As the source of the feedback control system, the Inertial navigation system/Global position system (INS/GPS) is the premise of flight control system security. However, unmanned aerial vehicles have the requirement of lightweight and low cost for airborne equipment, which makes redundant device object unrealistic. Therefore, the method of fault detection and diagnosis is desperately needed. In this paper, a fault detection and diagnosis method based on fuzzy system and neural network is proposed. Fuzzy system does not depend on the mathematical model of the process, which overcomes the difficulties in obtaining the accurate model of unmanned aerial vehicles. Neural network has a strong self-learning ability, which could be used to optimize the membership function of fuzzy system. This paper is structured as follows: first, a Kalman filter observer is introduced to calculate the residual sequences caused by different sensor faults. Then, the sequences are transmitted to the fault detection and diagnosis system and fault type can be obtained. The proposed fault detection and diagnosis algorithm was implemented and evaluated with real datasets, and the results demonstrate that the proposed method can detect the sensor faults successfully with high levels of accuracy and efficiency.

scholarly journals Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4140
Author(s):  
Weiguo He ◽  
Deyang Yin ◽  
Kaifeng Zhang ◽  
Xiangwen Zhang ◽  
Jianyong Zheng
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Naive Bayes ◽  
Fault Detection And Diagnosis ◽  
Fine Tuning ◽  
Pv Systems ◽  
Bayes Model ◽  
Pv Array ◽  
Diagnosis Method ◽  
Detection And Diagnosis

With the widespread attention and research of distributed photovoltaic (PV) systems, the fault detection and diagnosis problems of distributed PV systems has become increasingly prominent. To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault conditions of PV array such as open-circuit, short-circuit, shading, abnormal degradation, and abnormal bypass diode is proposed. First, in view of the problem of less distributed PV fault data, a fine-tuning Naive Bayes model (FTNB) is proposed to improve the diagnosis accuracy. Second, the failure sample set is used to train the model. Then, the maximum power point data of the PV inverter and the meteorological data are collected for fault diagnosis. Finally, the effectiveness and accuracy of the proposed method are verified by the analysis of simulation. In addition, this method requires only a small number of fault sample sets and no additional measurement equipment is required, which is suitable for real-time monitoring of distributed PV systems.

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