An artificial neural network based fault detection and diagnosis for wireless mesh networks

2018 ◽  
Author(s):  
Akmal Yaqini ◽  
Freshta Popalyar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Fault Detection And Diagnosis ◽  
Wireless Mesh ◽  
Artificial Neural ◽  
Detection And Diagnosis

A Method of Fault Detection and Diagnosis for Electromagnetic Direction Valve Based on Artificial Neural Network

2012 ◽  
Vol 476-478 ◽  
pp. 2384-2388
Author(s):  
Min Qiang Dai ◽  
Wei Cai ◽  
Sheng Dun Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Line Detection ◽  
On Line ◽  
Artificial Neural ◽  
Detection And Diagnosis

The magnetic field and vibration signal of electromagnetic direction valve can be detected real-timely by a non-intrusive on line detection device, which can use to monitor working state of the valve. A method of fault detection and diagnosis for electromagnetic direction valve from the signal detected by the non-intrusive on line detection device is presented in this paper. The wave frequency bands energy analysis method is adopted to distinguish the electromagnetic direction valve’s state, and the vibration signal are decomposed by three-layer wavelet packet which wavelet basis is db10. The fault identification method is based on BP artificial neural network (ANN), which is the most well-known three-layers BP ANN whose input and output layers have 8 and 3 neurons respectively.

scholarly journals Slantlet transform used for faults diagnosis in robot arm

2022 ◽  
Vol 25 (1) ◽  
pp. 281
Author(s):  
Muhamad Azhar Abdilatef Alobaidy ◽  
Jassim Mohammed Abdul-Jabbar ◽  
Saad Zaghlul Al-khayyt
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fault Detection And Diagnosis ◽  
Discrete Wavelet ◽  
Robot Arm ◽  
Multi Layer Perceptron ◽  
Artificial Neural ◽  
Detection And Diagnosis

<p class="JESTECAbstract">The <span>robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time <br /> (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same </span>purpose.</p>

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