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

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.

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

Symmetry ◽

10.3390/sym14010013 ◽

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.

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Rolling Bearing Fault Diagnosis Research

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.155-156.87 ◽

2012 ◽

Vol 155-156 ◽

pp. 87-91

Author(s):

Zhong Hu Yuan ◽

Yang Su ◽

Xiao Xuan Qi

Keyword(s):

Fault Diagnosis ◽

Time Domain ◽

Recognition Rate ◽

Principal Component ◽

Rolling Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Time Domain Signal ◽

Feature Information ◽

The Time Domain

According to the characteristics of the rolling bearing fault, we make the research on fault diagnosis. Time domain signal can not perform the fault feature information well. The power spectrum changes the time domain signals into the frequency signals. It sets up the new data model. It uses the principal component analysis on fault diagnosis. It uses T square statistics and Q statistics methods to make fault diagnosis. Simulation experiment results demonstrate that this method provides a high recognition rate.

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Application of modified culture Kalman filter in bearing fault diagnosis

Open Physics ◽

10.1515/phys-2018-0095 ◽

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.

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

Applied Sciences ◽

10.3390/app10124086 ◽

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.

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The application of time–frequency reconstruction and correlation matching for rolling bearing fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406215584397 ◽

2015 ◽

Vol 229 (17) ◽

pp. 3291-3295 ◽

Cited By ~ 6

Author(s):

Jian Xu ◽

Shuiguang Tong ◽

Feiyun Cong ◽

Yidong Zhang

Keyword(s):

Fault Diagnosis ◽

Frequency Distribution ◽

Noise Suppression ◽

Rolling Bearing ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Original Time ◽

Correlation Matching ◽

Time Frequency Distribution

There are still some remaining issues for time–frequency distribution application in rolling bearing fault diagnosis, such as noise suppression and resolution improvement. In this paper, we proposed a novel time–frequency correlation matching and reconstruction method to enhance the ability of rolling bearing fault identification. Firstly, we use the optimal simulated bearing fault signal to obtain the matching template through time–frequency distribution. Then, correlation matching operation is conducted between the obtained matching template and the original time–frequency distribution of analyzed signal. Finally, the original time–frequency distribution is reconstructed with the correlation coefficients and matching template using the template reconstruction algorithm. The reconstructed time–frequency distribution has inherited the capability of matching template in noise suppression, and can reveal the fault impulses of interest in a unified scale. The effectiveness of the proposed method has been proved by experimental result.

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