Harmonic Response Analysis of Rotor-Rolling Bearing-Base System for Fault Diagnosis of Rotating Machinery Based on Base

2011 ◽  
Vol 143-144 ◽  
pp. 669-674
Author(s):  
B. Qin ◽  
J.G. Wu ◽  
X.J. Li ◽  
B.H. Yao
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Response Analysis ◽  
Base System ◽  
Test Bed ◽  
Harmonic Response ◽  
Harmonic Response Analysis

In the fault diagnosis of rotating machinery through vibration analysis of the base, the signal may be weak and impure since the vibration signal which collected at the base is far away from the fault source. In order to provide useful evidences for the condition monitoring and fault diagnosis of rolling bearing based on the base vibration signal analysis, the rotor-bearing-base system model is built by taking the Spectra Quest comprehensive fault simulation test-bed as the object, the harmonic response analysis of the entire system is done with finite element analysis software ANSYS, and the ideal locations where sensors are installed on the base are obtained. These will form the foundation for the condition monitoring and fault diagnosis of rolling bearing based on the base vibration signal.

scholarly journals A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246905
Author(s):  
Chunming Wu ◽  
Zhou Zeng
Keyword(s):  
Fault Diagnosis ◽  
Noise Pollution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Generative Adversarial Network ◽  
High Background ◽  
Bearing Fault Diagnosis ◽  
Adversarial Network ◽  
Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

Vibration Signal Analysis of Bearing Based on EMD and Resonance Demodulation

2014 ◽  
Vol 556-562 ◽  
pp. 1286-1289 ◽  
Author(s):  
Jie Shi ◽  
Xing Wu ◽  
Nan Pan ◽  
Sen Wang ◽  
Jun Zhou
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Experimental Station ◽  
Fault Type ◽  
Mode Function ◽  
Vibration Signal Analysis

In order to monitor the operation state and implement fault diagnosis of rolling bearing in rotating machinery, this paper presents a method of fault diagnosis of rolling bearing, which is based on EMD and resonance demodulation. Using EMD to decompose the signal, which comes from QPZZ-II experimental station, the components of intrinsic mode function (IMF) will be obtained. Then, calculating the correlation coefficient of each IMF component, the highest correlation coefficient of IMF component will be analyzed by resonance demodulation. Finally, the experimental results show that the method can accurately identify and diagnose the running state and bearing fault type.

Rolling Bearing Fault Diagnosis with EMD-Based Fault Characteristic Frequency Difference Analysis

2014 ◽  
Vol 596 ◽  
pp. 437-441 ◽  
Author(s):  
Yan Ping Guo ◽  
Yu Xiong ◽  
Guo Cui Song
Keyword(s):  
Fault Diagnosis ◽  
Single Point ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Test Bed ◽  
Vibration Measurements ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Vibration Signal Analysis ◽  
Fault Characteristic

This paper presents a novel single-point rolling bearing fault diagnosis mechanism through vibration signal analysis. It is highlighted that the rolling bearing operational state can be well estimated by the first small set of Intrinsic Mode Function (IMF) components of the original vibration measurements through Empirical Mode Decomposition (EMD). These IMF components can be further translated into envelope spectrum by using Hilbert Transform. As a result, the difference of fault characteristic frequencies (DFCF) is derived to properly characterize different fault patterns for fault diagnosis. The suggested method is implemented and evaluated in a rolling bearing test bed for a range of failure scenarios (e.g. inner and outer raceway fault, rolling elements fault) with extensive vibration measurements. The result demonstrates that the proposed solution is effective for characterizing and detecting arrange of rolling bearing faults.quality).

The Comparison of Acoustic Emission with Vibration for Fault Diagnosis of the Bearing

2011 ◽  
Vol 141 ◽  
pp. 539-543 ◽  
Author(s):  
Xiao Liang Feng ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Chang Liu
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Significant Role ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Outer Race ◽  
Hilbert Envelope

The fault diagnosis of rolling bearing plays a significant role in rotating machinery. This paper makes a comparison between the acoustic emission and vibration signal in the fault diagnosis of the bearing of outer race pitting. The acoustic emission and vibration signal are processed by the wavelet transform, Hilbert envelope transform and FFT transform. Finally, the spectrum charts of the signals of acoustic emission and vibration are drew out. Based on the analysis results, the conclusion can be drawn that acoustic emission is superior to vibration in the fault diagnosis of the bearing.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 98
Author(s):  
Haodong Yuan ◽  
Nailong Wu ◽  
Xinyuan Chen ◽  
Yueying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
Diagnosis Method ◽  
Fault Feature Extraction

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision.

scholarly journals Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Longlong Li ◽  
Yahui Cui ◽  
Runlin Chen ◽  
Xiaolin Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Rotating Machinery ◽  
Accelerated Life Test ◽  
Rolling Element Bearing ◽  
Main Task ◽  
Detection Techniques

Rotating machinery has extensive industrial applications, and rolling element bearing (REB) is one of the core parts. To distinguish the incipient fault of bearing before it steps into serious failure is the main task of condition monitoring and fault diagnosis technology which could guarantee the reliability and security of rotating machinery. The early defect occurring in the REB is too weak and manifests itself in heavy surrounding noise, thus leading to the inefficiency of the fault detection techniques. Aiming at the vibration signal purification and promoting the potential of defects detection, a new method is proposed in this paper based on the combination of singular value decomposition (SVD) technique and squared envelope spectrum (SES). The kurtosis of SES (KSES) is employed to select the optimal singular component (SC) obtained by applying SVD to vibration signal, which provides the information of the REB for fault diagnosis. Moreover, the rolling bearing accelerated life test with the bearing running from normal state to failure is adopted to evaluate the performance of the SVD-KSES, and results demonstrate the proposed approach can detect the incipient faults from vibration signal in the natural degradation process.

Dynamic Analysis of DVG 850 High-Speed Machining Center

2012 ◽  
Vol 487 ◽  
pp. 203-207
Author(s):  
Gong Xue Zhang ◽  
Xiao Kai Shen
Keyword(s):  
High Speed ◽  
Simulation Analysis ◽  
Response Analysis ◽  
Improved Method ◽  
Analysis Software ◽  
Element Analysis ◽  
Harmonic Response ◽  
Machining Center ◽  
Calculation Results ◽  
Harmonic Response Analysis

Purpose, with the application of workbench finite element analysis software, get the analysis results of DVG 850 high-speed vertical machining center via the modal analysis and harmonic response analysis. Use the calculation results for reference, put forward the improved method, and prove the credibility of the simulation analysis by testing DVG 850 prototype.

scholarly journals Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

2021 ◽  
pp. 095440622110165
Author(s):  
Zhang Chao ◽  
Wang Wei-zhi ◽  
Zhang Chen ◽  
Fan Bin ◽  
Wang Jian-guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Short Term Memory ◽  
Vibration Signal ◽  
Short Term ◽  
Global Features ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

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