scholarly journals Actuator Fault Diagnosis with Robustness to Sensor Distortion

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
Qinghua Zhang
Keyword(s):  
Fault Diagnosis ◽  
New Method ◽  
Actuator Fault ◽  
Actuator Faults ◽  
Numerical Example ◽  
Diagnosis Method ◽  
Essential Assumption

Actuator fault diagnosis is often studied under strong assumptions on available sensors. Typically, it is assumed that the sensors are either fault free or sufficiently redundant. The purpose of this paper is to present a new method foractuatorfault diagnosis which is robust tosensordistortion. It does not require sensor redundancy to compensate sensor distortion. The essential assumption is that sensor distortions are strictly monotonous. Despite the nonlinear and unknown nature of distortions, such sensors still provide useful information for fault diagnosis. The robustness of the presented diagnosis method is analyzed, as well as its ability to detect actuator faults. A numerical example is provided to illustrate its efficiency.

scholarly journals Actuator Fault Diagnosis for Discrete-Time Systems via Augmenting State Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yongchao Wang ◽  
Shangmin Qi ◽  
Yujun Hu ◽  
Shenghui Guo ◽  
Darong Huang
Keyword(s):  
Fault Diagnosis ◽  
Interval Estimation ◽  
Descriptor System ◽  
Linear Matrix ◽  
Reconstruction Method ◽  
Actuator Fault ◽  
Actuator Faults ◽  
System Matrix ◽  
Interval Observer ◽  
Time Systems

For the problem of the actuator fault diagnosis in the control systems, this paper presents a novel method by using an interval estimation approach to detect the faults and reconstruct them. In order to make estimations of the unavoidable measurement noise, a descriptor system form is built. Firstly, a full-order interval observer is developed to detect actuator faults for its sensitiveness to them. Then, a reduced-order one, which is robust to actuator faults, is presented. This method does not need the boundary information of faults; thus, the design condition is more relaxed. In order to make the interval observer stable and cooperative, linear matrix inequalities and a time-varying transformation are employed to ensure the error system matrix to be Schur and nonnegative. Based on the interval estimation results of the aforementioned method, an interval reconstruction method of actuator faults is proposed. Finally, results of the two simulation examples verify the proposed methods are effective and accurate.

scholarly journals Power Grid Fault Diagnosis Method Using Intuitionistic Fuzzy Petri Nets Based on Time Series Matching

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Mingyue Tan ◽  
Jiming Li ◽  
Xiangqian Chen ◽  
Xuezhen Cheng
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Petri Nets ◽  
Power Grid ◽  
New Method ◽  
Uncertain Information ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Petri Nets ◽  
Diagnosis Method ◽  
Diagnosis Model

To improve the reliability of power grid fault diagnosis by enhancing the processing ability of uncertain information and adequately utilizing the alarm information about power grids, a fault diagnosis method using intuitionistic fuzzy Petri Nets based on time series matching is proposed in this paper. First, the alarm hypothesis sequence and the real alarm sequence are constructed using the alarm information and the general grid protection configuration model, and the similarity of the two sequences is used to calculate the timing confidence. Then, an intuitionistic fuzzy Petri Nets fault diagnosis model, with an excellent ability to process uncertain information from intuitionistic fuzzy sets, is constructed, and the initial place value of the model is corrected by the timing confidence. Finally, an application of the fault diagnosis model for the actual grid is established to analyze and verify the diagnostic results of the new method. The results for some test cases show that the new method can improve the accuracy and fault tolerance of fault diagnosis, and, furthermore, the abnormal state of the component can be inferred.

scholarly journals Sliding Mode Thau Observer for Actuator Fault Diagnosis of Quadcopter UAVs

2018 ◽  
Vol 8 (10) ◽  
pp. 1893 ◽  
Author(s):  
Ngoc Phi Nguyen ◽  
Sung Kyung Hong
Keyword(s):  
Fault Diagnosis ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
State Equation ◽  
Linear Matrix ◽  
Time Variability ◽  
Actuator Faults ◽  
External Disturbances ◽  
Aerial Vehicle ◽  
Diagnosis Method

Fault diagnosis (FD) is one of the main roles of fault-tolerant control (FTC) systems. An FD should not only identify the presence of a fault, but also quantify its magnitude and location. In this work, we present a robust fault diagnosis method for quadcopter unmanned aerial vehicle (UAV) actuator faults. The state equation of the quadcopter UAV is examined as a nonlinear system. An adaptive sliding mode Thau observer (ASMTO) method is proposed to estimate the fault magnitude through an adaptive algorithm. We then obtain the design matrices and parameters using the linear matrix inequalities (LMI) technique. Finally, experimental results are presented to show the advantages of the proposed algorithm. Unlike previous research on quadcopter UAV FD systems, our study is based on ASMTO and can, therefore, determine the time variability of a fault in the presence of external disturbances.

The Weighting Grey Relational Analysis of the Fault Diagnosis of Frame Cross’s Fracture

2011 ◽  
Vol 65 ◽  
pp. 255-259 ◽  
Author(s):  
Yan Zhang ◽  
Hui Song ◽  
Guan Jun Meng ◽  
Yan Wang
Keyword(s):  
Fault Diagnosis ◽  
Grey Relational Analysis ◽  
Fault Tree Analysis ◽  
New Method ◽  
Relational Analysis ◽  
Accident Probability ◽  
Diagnosis Method ◽  
Incidence Analysis ◽  
Grey Relational ◽  
The Relationship

Fault tree analysis is a fault diagnosis method that is better suited for "top-down" analysis. It can effectively evaluate cause-and-effect relationship and accident probability. In this paper, a new method of weighting grey relational analysis was applied in FTA. The principles of grey incidence analysis were introduced in detail. And the new method was used in the analysis of automobile frame cross’s fracture. The relationship between the system’s failure characters and its inside characters was found, and the hazardous events were worked out. The example results can prove that the weighting grey relational analysis of FTA is available and practicable, and the diagnosis results are reliable.

Rolling Bearing Fault Diagnosis Based on Blind Source Separation

2012 ◽  
Vol 217-219 ◽  
pp. 2546-2549 ◽  
Author(s):  
Chang Zheng Chen ◽  
Qiang Meng ◽  
Hao Zhou ◽  
Yu Zhang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Rolling Bearing ◽  
New Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

This document presents fault diagnosis method of rolling bearing based on blind source separation. The algorithm based on fast ICA is improved to separate fault signals according to the rolling bearing’s fault characteristics. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

Multi-Fractal Based Fault Diagnosis Method of Rotating Machinery

2011 ◽  
Vol 130-134 ◽  
pp. 571-574
Author(s):  
Shu Qing Zhang ◽  
Yu Zhu He ◽  
Jin Min Zhang ◽  
Yu Chun Zhao
Keyword(s):  
Fault Diagnosis ◽  
Experimental Analysis ◽  
Fractal Theory ◽  
Rotating Machinery ◽  
New Method ◽  
Vibration Signals ◽  
Diagnosis Method ◽  
Fractal Spectrum ◽  
Fractal Characteristics ◽  
Complex Features

Aiming at complex features of the fault rotating machinery such as nonstationary and nonlinearity, a new method for fault diagnosis based on multi-fractal was introduced. The vibration signals firstly are analyzed by multi-fractal theory and have multi-fractal characteristics. Then the area of multi-fractal spectrum S and the entropy of multi-fractal spectrum Hm were extracted as new criterions to diagnose machinery faults. Results of experimental analysis indicate that the method is effective and it provides a new way in fault diagnosis of rotating machinery.

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.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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