1409 Development of the diagnostic system for rotating machinery : Diagnostics of defect of rolling bearing

2005 ◽  
Vol 2005.80 (0) ◽  
pp. _14-17_-_14-18_
Author(s):  
Hideaki IWAMOTO ◽  
Takuzo IWATSUBO
Keyword(s):  
Diagnostic System ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Machinery Diagnostics

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.

Introduce the Quantitative Identification Method of Rolling Bearing in the Application of Fault Detection

2015 ◽  
Vol 742 ◽  
pp. 147-149
Author(s):  
Li Huo
Keyword(s):  
Rolling Bearing ◽  
Rotating Machinery ◽  
Normal Operation ◽  
Support Vector ◽  
Decision Boundary ◽  
Proposed Model ◽  
Quantitative Identification ◽  
Diagnosis Model ◽  
Bearing Damage ◽  
Vector Description

Rolling bearing is an important part of rotating machinery. Its failure will directly affect the normal operation of the whole machinery. This study proposed an intelligent diagnosis model based on Fuzzy support vector description for the quantitative identification of bearing fault. The proposed model constructs the spherically shaped decision boundary by training the features of normal bearing data, and then calculates the fuzzy monitoring coefficient to identify the bearing damage.

Fault Diagnosis Methods of Rolling Bearing: A General Review

2011 ◽  
Vol 480-481 ◽  
pp. 986-992 ◽  
Author(s):  
Xiao Xuan Qi ◽  
Jian Wei Ji ◽  
Xiao Wei Han
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Rolling Bearings ◽  
Signal Process ◽  
Existing Problems ◽  
Bearing Failures ◽  
Non Stationary Signal ◽  
Fault Mechanism ◽  
Research Situation

Rolling bearing failures account for most of rotating machinery failures. Fault diagnosis of rolling bearings according to their running state is of great importance. In this paper current research situation and existing problems of fault diagnosis are summarized firstly. Then several different diagnosis approaches in terms of the measuring medium are reviewed. After analysis of fault mechanism, feature extraction based on non-stationary signal process is elaborated. Finally, the development tendencies are pointed out.

scholarly journals Vibrational Diagnostics of Rotating Machinery Malfunctions

1995 ◽  
Vol 1 (3-4) ◽  
pp. 237-266 ◽  
Author(s):  
Agnes Muszynska
Keyword(s):  
Rotating Machinery ◽  
Vibration Monitoring ◽  
Process Data ◽  
Vibration Data ◽  
Machine Monitoring ◽  
Vibration Responses ◽  
Actual Field ◽  
Vibrational Diagnostics ◽  
Monitoring And Diagnostics ◽  
Machinery Diagnostics

This paper outlines rotating machinery malfunction diagnostics using vibration data in correlation with operational process data. The advantages of vibration monitoring systems as a part of preventive/predictive maintenance programs are emphasized. After presenting basic principles of machinery diagnostics, several specific malfunction symptoms supported by simple mathematical models are given. These malfunctions include unbalance, excessive radial load, rotor-to-stator rubbing, fluid-induced vibrations, loose stationary and rotating parts, coupled torsional/lateral vibration excitation, and rotor cracking. The experimental results and actual field data illustrate the rotor vibration responses for individual malfunctions. Application of synchronous and nonsynchronous perturbation testing used for identification of basic dynamic characteristics of rotors is presented. Future advancements in vibration monitoring and diagnostics of rotating machinery health are discussed. In the Appendix, basic instrumentation for machine monitoring is outlined.

Life Prediction of Rolling Bearing Using Genetic Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 2423-2427 ◽  
Author(s):  
Peng Chen ◽  
Yuki Koide ◽  
Ke Li ◽  
Noriaki Satonaga
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Life Prediction ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Industrial Plant ◽  
Plant Safety ◽  
Operation Stability ◽  
Searching Method ◽  
Condition Diagnosis

Rolling bearing is the most part used in rotating machinery of industrial plant. The condition diagnosis technology of rolling bearing is very important and indispensable for the plant safety and operation stability. The purpose of this study is to improve the accuracy of the life prediction for a rolling bearing. The paper proposes a searching method for the optimum mathematic function for the high accurate prediction by using the genetic algorithms (GA).

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.

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