scholarly journals Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Runxia Guo ◽  
Kai Guo ◽  
Quan Gan ◽  
Junwei Zhang ◽  
Jiankang Dong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Control Systems ◽  
Asymptotical Stability ◽  
Measurement Noise ◽  
Diagnostic Strategy ◽  
State Variables ◽  
External Disturbances ◽  
Practical Engineering ◽  
Diagnosis Algorithm

The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

scholarly journals A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM

2021 ◽  
Author(s):  
Liam Biddle ◽  
Saber Fallah
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Control Systems ◽  
Autonomous Vehicle ◽  
Detection Algorithm ◽  
Support Vector ◽  
Isolation And Identification ◽  
Multiple Faults ◽  
Predictive Algorithm ◽  
Prediction Approach

AbstractFaults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient computational burden for real-time implementation. Support Vector Machine techniques are used to detect and identify faults in sensors for autonomous vehicle control systems. In addition, to identify degrading performance in a sensor and predict the time at which a fault will occur, a novel predictive algorithm is proposed. The effectiveness and accuracy of the architecture in detecting and identifying multiple faults as well as the accuracy of the proposed predictive fault detection algorithm are verified through a MATLAB/IPG CarMaker co-simulation platform. The results present detection and identification accuracies of 94.94% and 97.01%, respectively, as well as a prediction accuracy of 75.35%.

Model-Based Fault-Diagnosis of Electronic Throttle System

2012 ◽  
Vol 220-223 ◽  
pp. 1084-1088 ◽  
Author(s):  
Jian Wu ◽  
Yang Zhao
Keyword(s):  
Fault Diagnosis ◽  
Control Systems ◽  
Digital Filtering ◽  
State Equation ◽  
Fault Analysis ◽  
Electronic Throttle ◽  
First Order ◽  
Diagnosis Algorithm ◽  
Proper Performance ◽  
Motor Model

The designed electronic throttle control systems are able to diagnose faults, which affects the proper performance of the system. In this study, a fault-diagnosis algorithm of electronic throttle system based on parameter estimation is presented, using a simplified version of the DC motor model so as to minimize the relevant computation. By means of the Butterworth digital filtering state equation, the signal’s filtering value as well as its first-order and second-order derivatives are obtained. Thus, the operational precision is improved and the error influence from derivative discretization is avoided. The experimental results are consistent with the proposed algorithm employed in fault analysis.

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.

Diagnostic Fault Simulation for the Failure Analyst

2004 ◽  
Author(s):  
Dan Bodoh ◽  
Anthony Blakely ◽  
Terry Garyet
Keyword(s):  
Fault Diagnosis ◽  
Failure Analysis ◽  
Complete Solution ◽  
Fault Simulation ◽  
Building Blocks ◽  
Physical World ◽  
Design For Test ◽  
Fault Diagnosis System ◽  
Diagnosis Algorithm ◽  
Diagnosis Tool

Abstract Since failure analysis (FA) tools originated in the design-for-test (DFT) realm, most have abstractions that reflect a designer's viewpoint. These abstractions prevent easy application of diagnosis results in the physical world of the FA lab. This article presents a fault diagnosis system, DFS/FA, which bridges the DFT and FA worlds. First, it describes the motivation for building DFS/FA and how it is an improvement over off-the-shelf tools and explains the DFS/FA building blocks on which the diagnosis tool depends. The article then discusses the diagnosis algorithm in detail and provides an overview of some of the supporting tools that make DFS/FA a complete solution for FA. It also presents a FA example where DFS/FA has been applied. The example demonstrates how the consideration of physical proximity improves the accuracy without sacrificing precision.

Efficient Fault Diagnosis Algorithm in Dynamic Internet Service Environment

2009 ◽  
Vol 20 (9) ◽  
pp. 2520-2530
Author(s):  
Ling-Wei CHU ◽  
Shi-Hong ZOU ◽  
Shi-Duan CHENG ◽  
Chun-Qi TIAN ◽  
Wen-Dong WANG
Keyword(s):  
Fault Diagnosis ◽  
Internet Service ◽  
Service Environment ◽  
Diagnosis Algorithm

Optimal Fault Diagnosis for Networked Control Systems with Large Time-delays and Noises

2012 ◽  
Vol 38 (5) ◽  
pp. 858-864 ◽  
Author(s):  
Juan LI ◽  
You-Gang ZHAO ◽  
Yang YU ◽  
Peng ZHANG ◽  
Hong-Wei GAO
Keyword(s):  
Fault Diagnosis ◽  
Control Systems ◽  
Time Delays ◽  
Large Time ◽  
Networked Control Systems ◽  
Networked Control

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Orthotopic-filtering-based hierarchical fault diagnosis algorithm for linear recursive models

2020 ◽  
Vol 14 (16) ◽  
pp. 2310-2318
Author(s):  
Ziyun Wang ◽  
Guixiang Xu ◽  
Yan Wang ◽  
Ju H. Park ◽  
Zhicheng Ji
Keyword(s):  
Fault Diagnosis ◽  
Recursive Models ◽  
Diagnosis Algorithm
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