Neural Network Based Adaptive Actuator Fault Detection Algorithm for Robot Manipulators

2018 ◽  
Vol 95 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Chang Nho Cho ◽  
Ji Tae Hong ◽  
Hong Ju Kim
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Robot Manipulators ◽  
Detection Algorithm ◽  
Actuator Fault

Actuator Fault Detection and Isolation for Robot Manipulators with the Adaptive Observer

2012 ◽  
Vol 482-484 ◽  
pp. 529-532
Author(s):  
Shao Cong Guo ◽  
Mo Han Yang ◽  
Zi Rui Xing ◽  
Yi Li ◽  
Ji Qing Qiu
Keyword(s):  
Fault Detection ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Experimental Results ◽  
Fault Detection And Isolation ◽  
Adaptive Observer ◽  
Actuator Fault ◽  
Unknown Parameters ◽  
State Observers ◽  
Planar Robot

The fault detection and isolation (FDI) for industrial robot manipulators, subject to faults of actuator, is devised in this paper. An adaptive observer is designed to tackle the robustness problem for unknown parameters due to faults，based on a bank of state observers. By using an adaptive regulating algorithm, the observer is ensured to be stable and the estimated errors are guaranteed to converge. Experimental results are reported for a planar robot under gravity, considering partial failures of the motor torques.

scholarly journals Adaptive State Observer for Robot Manipulators Diagnostics and Health Degree Assessment

2020 ◽  
Vol 10 (2) ◽  
pp. 514 ◽  
Author(s):  
Sanlei Dang ◽  
Zhengmin Kong ◽  
Long Peng ◽  
Yilin Ji ◽  
Yongwang Zhang
Keyword(s):  
Fault Detection ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Health Assessment ◽  
Dynamic Environment ◽  
Detection Algorithm ◽  
Real System ◽  
Adaptive Observer ◽  
Output Measurement ◽  
The Real

To avoid serious damages caused by the dynamic environment, fault detection and health assessment are essential for an integrated robotic system. In this paper, we propose a fault detection algorithm and a health degree assessment approach for a robot manipulator system. Both the internal disturbance and the output measurement disturbance are considered in the proposed method. In addition, an adaptive observer is utilized to reconstruct the real system of robot manipulators. Under the proposed observer, the real system is estimated to detect the fault and obtain the health degree of the robot manipulator. The feasibility and reliability of the proposed fault detection algorithm and health degree assessment index for robot manipulator systems are proved by simulation experiments.

Vehicle sensor and actuator fault detection algorithm for automated vehicles

2015 ◽  
Author(s):  
Yonghwan Jeong ◽  
Kyuwon Kim ◽  
Beomjun Kim ◽  
Jihyun Yoon ◽  
Hyokjin Chong ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection Algorithm ◽  
Actuator Fault ◽  
Automated Vehicles ◽  
Vehicle Sensor

Optimal Actuator Fault Detection via MLP Neural Network for PDFs

2005 ◽  
pp. 550-555 ◽  
Author(s):  
Lei Guo ◽  
Yumin Zhang ◽  
Chengliang Liu ◽  
Hong Wang ◽  
Chunbo Feng
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Actuator Fault ◽  
Mlp Neural Network

scholarly journals Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zuoxun Wang ◽  
Liqiang Xu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Power System ◽  
Fault Location ◽  
Detection Algorithm ◽  
Travelling Wave ◽  
Discrete Wavelet ◽  
Chaotic Neural Network ◽  
Transition Resistance

The safety and stability of the power supply system are affected by some faults that often occur in power system. To solve this problem, a criterion algorithm based on the chaotic neural network (CNN) and a fault detection algorithm based on discrete wavelet transform (DWT) are proposed in this paper. MATLAB/Simulink is used to establish the system model to output fault signals and travelling wave signals. Db4 wavelet decomposes the travelling wave signals into detail signals and approximate signals, and these signals are combined with the two-terminal travelling wave location method to achieve fault location. And the wavelet detail coefficients are extracted to input to the proposed chaotic neural network. The results show that the criterion algorithm can effectively determine whether there are faults in the power system, the fault detection algorithm has the capabilities of locating the system faults accurately, and both algorithms are not affected by fault type, fault location, fault initial angle, and transition resistance.

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