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 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 Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis

2019 ◽  
Vol 9 (9) ◽  
pp. 1852 ◽  
Author(s):  
Hua Ding ◽  
Yiliang Wang ◽  
Zhaojian Yang ◽  
Olivia Pfeiffer
Keyword(s):  
Blind Source Separation ◽  
Additive Noise ◽  
Rbf Neural Network ◽  
Source Separation ◽  
Mining Machine ◽  
Vibration Source ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Strongly Nonlinear ◽  
Nonlinear Mixtures

Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind source separation (BSS) algorithm to separate the critical independent sources associated with the transmission fault vibrations. For this reason, a new method based on wavelet de-noising and nonlinear independent component analysis (ICA) is presented in this paper to tackle the nonlinear BSS problem with additive noise. The wavelet de-noising approach was first employed to eliminate the influence of the additive noise in the BSS procedure. Then, the radial basis function (RBF) neural network combined with the linear ICA was applied to the de-noised vibration signals. Vibration sources involved with the machine faults were separated. Subsequently, wavelet package decomposition (WPD) was used to extract distinct fault features from the source signals. Lastly, an RBF classifier was used to recognize the fault patterns. Field data acquired from a mining machine was used to evaluate and validate the proposed diagnostic method. The experimental analysis results show that critical fault vibration source component can be separated by the proposed method, and the fault detection rate is superior to the linear ICA based approaches.

Research on Identification Method of Multi-Mixed Vibration Signal of Aeroengine

2009 ◽  
Vol 419-420 ◽  
pp. 801-804
Author(s):  
Xiang Yang Jin ◽  
Shi Sheng Zhong
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signal ◽  
Signal Separation ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Vibration Data ◽  
Monitoring And Diagnosis ◽  
Rotating Shafts

The effectiveness of separation and identification of mechanical signals vibrations is crucial to successful fault diagnosis in the condition monitoring and diagnosis of complex machines.Aeroengine vibration signals always include many complicated components, blind source separation (BSS) provides a efficient way to separate the independent component.In order to get the most effective algorithm of vibration signal separation,experiment has been done to acquire plenty of multi-mixed rotor vibration signals,three sets of vibration data generated from aeroengine rotating shafts were separated from the synthetic vibration signal. The results prove that blind source separation is effective and can be applied for vibration signal processing and fault diagnosis of aeroengine.

Gear Faults Diagnosis Based on Nonlinear Blind Source Separation

2012 ◽  
Vol 548 ◽  
pp. 507-510
Author(s):  
Xiao Wei Wang ◽  
Lin Suo Shi ◽  
Shuang Chen ◽  
Hui Li ◽  
Wei Zhang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Learning Rate ◽  
Variable Rate ◽  
Vibration Signals ◽  
Long Time ◽  
Faults Diagnosis ◽  
Iterative Error

The nonlinear blind source separation is a practical and effective method in processing mechanical vibration signal, but it has the limitation which learning rate is fixed. It will take a long time for iterative parameters to get convergence. In this paper, a variable rate nonlinear BSS is proposed. The learning rate of the algorithm is adjusted based on iterative error in the different stopping iterating time and inverse proportion. The proposed algorithm increasing the efficiency of the nonlinear BSS and de-noising the vibration signals. Experiment on gears shows that the signal gained by the method more impersonality represents the gear condition

Feature Extraction of Rolling Bearing Based on LMD Energy Feature

2017 ◽  
Vol 868 ◽  
pp. 363-368
Author(s):  
Bang Sheng Xing ◽  
Le Xu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Small Samples ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Local Mean ◽  
Energy Feature

For the situation that it is difficult to diagnose rolling bearings fault effectively for small samples, so it proposes a feature extraction method of rolling bearing based on local mean decomposition (LMD) energy feature. Due to the frequency domain distribution of vibration signals will change when different faults occur in rolling bearings, so it can use LMD energy feature method to extract the fault features of rolling bearings. The instances analysis and extracted results show that the LMD energy feature can extract the vibration signal fault feature of rolling bearings effectively.

scholarly journals Blind Source Separation Method for Bearing Vibration Signals

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 658-664 ◽  
Author(s):  
He Jun ◽  
Yong Chen ◽  
Qing-Hua Zhang ◽  
Guoxi Sun ◽  
Qin Hu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Separation Method ◽  
Vibration Signals ◽  
Bearing Vibration

The Use of Blind-Source-Separation Algorithm for Mechanical Signal Separation and Machine Fault Diagnosis

2003 ◽  
Author(s):  
Peter W. Tse ◽  
Jinyu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Condition Monitoring ◽  
Source Separation ◽  
Simulated Data ◽  
Vibration Signal ◽  
Advantages And Disadvantages ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Mechanical Components

Vibration based machine fault diagnosis is widely adopted in machine condition monitoring. Since a machine is usually composed of many mechanical components, during the machine running, each component will generate its vibration and transmit to other components thru the shaft or linkages. Hence, the vibration signal collected from a sensor is the aggregation of all generated vibrations. To enhance the accuracy in vibration based machine fault diagnosis, the vibration generated by each component must be isolated and identified. In this paper, the performance of blind-source-separation (BSS) in separating various mixed sources is discussed. The BSS based method of second order statistics (SOS) has been applied to separate the aggregated vibration signals generated from a number of mechanical components. To verify the effectiveness of the BSS based SOS, a number of experiments were conducted using both simulated data and vibration generated form the industrial machines. The results show that the BSS possesses the ability to separate both artificially and naturally mixed signals. Such ability is definitely welcome in the fields of condition monitoring and maintenance. Moreover, the paper also discusses the advantages and disadvantages of the algorithm in the applications of machine fault diagnosis and future improvements.

Tool Breakage Feature Extraction in PCB Micro-Hole Drilling Using Vibration Signals

2012 ◽  
Vol 497 ◽  
pp. 126-131 ◽  
Author(s):  
Zhen Hua Ren ◽  
Xiao Hu Zheng ◽  
Qing Long An ◽  
Cheng Yong Wang ◽  
Ming Chen
Keyword(s):  
Feature Extraction ◽  
Printed Circuit Board ◽  
Vibration Signal ◽  
Circuit Board ◽  
Hole Drilling ◽  
Features Selection ◽  
Tool Breakage ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Micro Hole

Tool breakage monitoring is crucial to automation fabrication, especially for high-density hole machining, such as PCB (Printed Circuit Board). A tool breakage feature extraction method in PCB micro-hole drilling is presented in this paper. The vibration signal is analyzed by wavelet transform. The decomposed signals energy ratio at each frequency band is computed as monitoring features. The monitoring performance of different features selection is given. The vibration signals are observed to provide the capability in distinguishing micro drill breakage with proper features extraction and classifier design.

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