A novel method of the fiber-link fault detection and location with multi-channel and multi-fiber based on chaotic light theory

2014 ◽  
Author(s):  
Xinyu Dou ◽  
Hehe Yue ◽  
Yusong Zhou ◽  
Hongxi Yin ◽  
Chenguang Wu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Novel Method ◽  
Fiber Link ◽  
Light Theory

Fault detection performance of multi-mode transmission reflection analysis for few-mode fiber link

2021 ◽  
Author(s):  
Wenping Zhang ◽  
Feng Liu ◽  
Zhenxing He ◽  
Lixin Xu ◽  
Guijun Hu
Keyword(s):  
Fault Detection ◽  
Detection Performance ◽  
Fiber Link ◽  
Multi Mode

A Novel Method to Compensate Fiber Nonlinearities by Using NAFO Model

2018 ◽  
Vol 39 (4) ◽  
pp. 387-392
Author(s):  
Kuldeep Singh ◽  
Sandeep K. Arya
Keyword(s):  
Wiener Filter ◽  
The Novel ◽  
Third Order ◽  
Fiber Nonlinearities ◽  
Iir Filter ◽  
Bee Colony ◽  
Efficient Performance ◽  
Novel Method ◽  
Fiber Link ◽  
The Mean

Abstract This paper proposes the Nonlinear Adaptive Filter bank–based artificial bee colony Optimizer (NAFO) based on Wiener filter for inverse modeling of nonlinear fiber link. Here a third-order inverse Volterra-based nonlinear equalizer (VNLE) is used to compensate the fiber nonlinearities using MATLAB Simulink. In the novel method the Mean Square Error (MSE) of the optical link is measured and compared with IIR-filter based technique. The MSE is reported to be 10–14 value lesser in the novel experimental setup consequently the proposed technique shows enhanced and efficient performance.

Study on Fault Signal Detection for PMLSM

2011 ◽  
Vol 84-85 ◽  
pp. 442-446
Author(s):  
Bao Yu Xu ◽  
Xiao Zhuo Xu ◽  
Yi Lun Liu ◽  
Xu Dong Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Frequency Band ◽  
Relative Energy ◽  
Normalization Method ◽  
Current Signal ◽  
Novel Method ◽  
Artificial Neural

Based on wavelet transform and artificial neural network, a novel method which takes advantage of both the multi-resolution decomposition of wavelet transform and the classification characteristics of artificial neural network is proposed for fault detection of permanent magnet linear synchronous motor (PMLSM). According to the characteristic of unhealthy PMLSM, the wavelet transform is carried out to decompose and reconstruct winding current signal. Then the energy of each frequency band as faulty features can be detected through spectrum analysis of wavelet coefficients about each frequency band. With normalization method, the feature vectors are constructed from relative energy for energy of each frequency band. The proposed method is applied to the fault detection of PMLSM, and the result of simulation proved that the wavelet neural network can effectively detect different conditions of PMLSM.

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