scholarly journals A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Feng Miao ◽  
Rongzhen Zhao
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Diagnosis Method ◽  
Machine Health Monitoring ◽  
Machine Health

When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It is difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. In these environments, the vibration signals are first denoised with the synchronous cumulative average noise reduction (SCA) method, and the denoised signals were separated with the improved fast independent component analysis (FastICA) algorithm. The results of simulation test and rotor fault experiments demonstrate that the novel method can effectively extract fault features, certifying its superiority in comparison with previous methods. Therefore, it is likely to be useful and practical in the fault detection area, especially under the condition of strong noise and vibration interferences.

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.

The Research and Simulation of Blind Source Separation Algorithm

2016 ◽  
Vol 8 (3) ◽  
pp. 1-36
Author(s):  
Tao Gao ◽  
Jincan Li
Keyword(s):  
Blind Source Separation ◽  
Speech Enhancement ◽  
Source Separation ◽  
Separation Algorithm ◽  
Original Source ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Performance Evaluation Index ◽  
The Mathematical Model ◽  
Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

scholarly journals Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1192 ◽  
Author(s):  
Yaqin Xie ◽  
Jiayin Yu ◽  
Xinwu Chen ◽  
Qun Ding ◽  
Erfu Wang
Keyword(s):  
Image Restoration ◽  
Blind Source Separation ◽  
Source Separation ◽  
Signal Recovery ◽  
Separation Performance ◽  
Underdetermined Blind Source Separation ◽  
Elimination Algorithm ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Source Signals

To reduce the consumption of receiving devices, a number of devices at the receiving end undergo low-element treatment (the number of devices at the receiving end is less than that at the transmitting ends). The underdetermined blind-source separation system is a classic low-element model at the receiving end. Blind signal extraction in an underdetermined system remains an ill-posed problem, as it is difficult to extract all the source signals. To realize fewer devices at the receiving end without information loss, this paper proposes an image restoration method for underdetermined blind-source separation based on an out-of-order elimination algorithm. Firstly, a chaotic system is used to perform hidden transmission of source signals, where the source signals can hardly be observed and confidentiality is guaranteed. Secondly, empirical mode decomposition is used to decompose and complement the missing observed signals, and the fast independent component analysis (FastICA) algorithm is used to obtain part of the source signals. Finally, all the source signals are successfully separated using the out-of-order elimination algorithm and the FastICA algorithm. The results show that the performance of the underdetermined blind separation algorithm is related to the configuration of the transceiver antenna. When the signal is 3 × 4 antenna configuration, the algorithm in this paper is superior to the comparison algorithm in signal recovery, and its separation performance is better for a lower degree of missing array elements. The end result is that the algorithms discussed in this paper can effectively and completely extract all the source signals.

The Research and Simulation of Blind Source Separation Algorithm

2020 ◽  
pp. 494-531
Author(s):  
Tao Gao ◽  
Jincan Li
Keyword(s):  
Blind Source Separation ◽  
Speech Enhancement ◽  
Source Separation ◽  
Separation Algorithm ◽  
Original Source ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Performance Evaluation Index ◽  
The Mathematical Model ◽  
Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

Rolling Bearing Fault Diagnosis Based on Blind Source Separation

2012 ◽  
Vol 217-219 ◽  
pp. 2546-2549 ◽  
Author(s):  
Chang Zheng Chen ◽  
Qiang Meng ◽  
Hao Zhou ◽  
Yu Zhang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Rolling Bearing ◽  
New Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

This document presents fault diagnosis method of rolling bearing based on blind source separation. The algorithm based on fast ICA is improved to separate fault signals according to the rolling bearing’s fault characteristics. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

scholarly journals Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram

2020 ◽  
Vol 10 (18) ◽  
pp. 6385 ◽  
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Shaft Speed ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Maintenance Schedules

Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful information from vibration signals, diagnosis of motor failures by maintenance engineers can be gradually replaced by an automatic detection process. Especially, state-of-the-art methods using deep learning have contributed significantly to automatic fault diagnosis. This paper proposes a novel method for diagnosing bearing faults and their degradation level under variable shaft speed. In the proposed method, vibration signals are represented by spectrograms to apply deep learning methods through preprocessing using Short-Time Fourier Transform (STFT). Then, feature extraction and health status classification are performed by a convolutional neural network (CNN), VGG16. According to our various experiments, our proposed method can achieve very high accuracy and robustness for bearing fault diagnosis even under noisy environments.

Gearbox Fault Diagnosis Based on AIS-ICA Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4476-4480
Author(s):  
Jin Ying Huang ◽  
Hong Xia Pan ◽  
Ya Hui Lu ◽  
Yue Li
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Separation Procedure ◽  
Vibration Signals ◽  
Immune Optimization ◽  
Immune Optimization Algorithm ◽  
Gearbox Vibration ◽  
Ica Algorithm ◽  
Fault Information

Immune theory is applied to independent component analysis in this paper. By elaborated on blind source separation procedure, the blind source separation based on immune optimization algorithm is put forward, that is AIS-ICA algorithm. The paper carried out simulation experiments of mixing and separation for four specific signals. The experimental results show that the convergence speed and separation precision is high, and it has good stability. The new algorithm is used to gearbox vibration signals for blind source separation and fault diagnosis, fault information carried by vibration signals enhanced. Results show that the algorithm used to separate vibration signals of gearbox can greatly enhance fault information, reducing difficulty of gearbox fault diagnosis.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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