scholarly journals Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)

2020 ◽  
Vol 10 (11) ◽  
pp. 3735 ◽  
Author(s):  
Feng Miao ◽  
Rongzhen Zhao ◽  
Leilei Jia ◽  
Xianli Wang
Keyword(s):  
Blind Source Separation ◽  
Impulse Noise ◽  
Median Filter ◽  
Source Separation ◽  
Rotating Machinery ◽  
Second Order ◽  
Time Signal ◽  
Blind Identification ◽  
Sensor Signals ◽  
Strong Impulse

Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.

scholarly journals A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1713 ◽  
Author(s):  
Feng Miao ◽  
Rongzhen Zhao ◽  
Xianli Wang ◽  
Leilei Jia
Keyword(s):  
Blind Source Separation ◽  
Median Filter ◽  
Source Separation ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Vibration Signals ◽  
Pulse Noise ◽  
Feature Extraction Method ◽  
Multiple Sensors ◽  
Signal Processing Methods

During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments.

scholarly journals Arduino Due Implementation of an Algorithm for Blind Source Separation using Matlab Simulink

2019 ◽  
Vol 9 (2) ◽  
pp. 3692-3696 ◽  
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Low Cost ◽  
Source Separation ◽  
Second Order ◽  
Blind Identification ◽  
Matlab Simulink ◽  
Reference Signals ◽  
Inputs And Outputs

This work describes the implementation of an algorithm of the blind source separation in the Adruino Due card as free and low cost material. The algorithm must be developed to adapt the inputs and outputs of the Arduino board to the actual installation. Using reference signals generated by two generators (GBF), this work has proved that the algorithm Second Order Blind Identification (SOBI) is the most efficient to analyze our signal database, so the separation is performed on the Arduino Due using the SOBI algorithm to validate its performance in real time.

scholarly journals Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhiwei Zhang ◽  
Hongyuan Gao ◽  
Jingya Ma ◽  
Shihao Wang ◽  
Helin Sun
Keyword(s):  
Objective Function ◽  
Blind Source Separation ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Source Separation ◽  
Hybrid Optimization ◽  
Slime Mould ◽  
Stable Performance

In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.

On the use of simulated annealing to automatically assign decorrelated components in second-order blind source separation

2006 ◽  
Vol 53 (5) ◽  
pp. 810-820 ◽  
Author(s):  
M. Bohm ◽  
K. Stadlthanner ◽  
P. Gruber ◽  
F.J. Theis ◽  
E.W. Lang ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Blind Source Separation ◽  
Source Separation ◽  
Second Order
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