scholarly journals Weak Signal Detection Method Based on the Coupled Lorenz System and Its Application in Rolling Bearing Fault Diagnosis

2020 ◽  
Vol 10 (12) ◽  
pp. 4086
Author(s):  
Guozheng Li ◽  
Nanlin Tan ◽  
Xiang Li
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Lorenz System ◽  
Signal To Noise Ratio ◽  
Rolling Bearing ◽  
Synchronization Error ◽  
New Method ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Rolling bearings are widely used in rotating machinery. Their fault feature signals are often submerged in strong noise and are difficult to identify. This paper presents a new method of bearing fault diagnosis that combines the coupled Lorenz system and power spectrum technology. The process is achieved in the following three steps. First, a synchronization system based on the Lorenz system is constructed using the driving-response method. Second, when the tested signal is connected to the driving end, the synchronization error between the two sub-chaotic systems is obtained. Finally, the power spectrum density of the synchronization error is calculated and compared with the corresponding fault characteristic frequency. The coupled Lorenz system makes full use of the noise immunity and nonlinear amplification of the chaotic system. The detection characteristics and feasibility of the new method are verified by simulation and actual measured vibration data. The result shows that the noise reduction effect of the coupled Lorenz system is obvious. This method can improve the signal-to-noise ratio of the tested signal and provide a new way to perform fault diagnosis of rolling bearings.

scholarly journals A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Gu ◽  
Jiawei Cao ◽  
Xin Song ◽  
Jian Yao
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bp Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Approach ◽  
Fault Diagnosis System ◽  
Original Time

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.

scholarly journals Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 13
Author(s):  
Jianpeng Ma ◽  
Chengwei Li ◽  
Guangzhu Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Rolling Bearing ◽  
Fusion Method ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Evolution Analysis ◽  
Comparison Results

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

scholarly journals Application of modified culture Kalman filter in bearing fault diagnosis

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 757-765
Author(s):  
Wang Hailun ◽  
Alexander Martinez
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Nonlinear Model ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Abstract Rolling bearings are an important part of rotary machines. They are used most widely in various mechanical sectors, which are among the most vulnerable components in machines. This paper uses CKF algorithm to compile a signal analysis system, analyses the vibration signal of the rolling bearing, extracts fault features, and realizes fault diagnosis. In order to improve the estimation accuracy of bearing fault diagnosis under nonlinear model, a nonlinear model of bearing fault diagnosis based on quaternion and low-accuracy high-noise sensors is established, and the attitude estimation has performed using the culture Kalman filter (CKF) algorithm. The sensor data comparison shows that the use of the volumetric Kalman filter algorithm can effectively improve the estimation accuracy of bearing fault diagnosis and stability. In this paper, the measured vibration signals of several groups of rolling bearings are analysed, and the signal characteristic frequency has extracted. The results show that using the analysis software designed in this paper, several typical faults of rolling bearings can be correctly identified.

scholarly journals Rolling Bearing Fault Vibration Signal Denoising Based on Adaptive Morphological Wavelet Perona–Malik Filter Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hao Li ◽  
Yifan Tan ◽  
Yun Pu
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Haar Wavelet ◽  
Signal Denoising ◽  
Second Derivative ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Noise Point

This paper proposes an adaptive Perona–Malik filtering algorithm based on the morphological Haar wavelet, which is used for vibration signal denoising in rolling bearing fault diagnosis with strong noise. First, the morphological Haar wavelet operator is utilized to presmooth the noisy signal, and the gradient of the presmooth signal is estimated. Second, considering the uncertainty of gradient at the strong noise point, a strong noise point recognition operator is constructed to adaptively identify the strong noise point. Third, the two-step gradient average value of the strong noise point in the same direction is used to substitute, and the second derivative is introduced into the diffusion coefficient. Finally, diffusion filtering is performed based on the improved Perona–Malik model. The simulation experiment result indicated that not only the algorithm can denoise effectively, but also the average gradient and second derivative in the same direction can effectively suppress the back diffusion of strong noise points to improve the denoising signal-to-noise ratio. The experimental results of rolling bearing show that the algorithm can adaptively filter out strong noise points and keep the information of peak in the signal well, which can improve the accuracy of rolling bearing fault diagnosis.

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 Bearing Fault Diagnosis Using Synthetic Quantitative Index-Based Adaptive Underdamped Stochastic Resonance

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Baochen Li ◽  
Rui Tong ◽  
Jianshe Kang ◽  
Kuo Chi
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Correlation Coefficient ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Quantitative Index ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Better Than

Stochastic resonance is like a nonlinear filter to detect the weak bearing fault-induced impulses that submerged in strong noises. Signal-to-noise ratio (SNR) is often used as the index to evaluate the SR output, but the fault characteristic frequency (FCF) must be known in order to calculate SNR. A novel bearing fault diagnosis method called synthetic quantitative index-based adaptive underdamped stochastic resonance (SQI-AUSR) is proposed. The synthetic quantitative index (SQI) is composed of power spectrum kurtosis, kurtosis, margin index, and correlation coefficient. The SQI is independent of FCF, which avoids the limitation that the calculation of SNR must know the FCF. Numeric simulations and two case studies of bearing faults are carried out. The results show that (1) the SQI is more effective than other proposed indexes such as correlation coefficient and weight power spectrum kurtosis and (2) the proposed SQI-AUSR is effective for bearing fault diagnosis and is better than SNR-AOSR.

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