Application of ANN, Fuzzy Logic and Wavelet Transform in machine fault diagnosis using vibration signal analysis

2010 ◽  
Vol 16 (2) ◽  
pp. 190-213 ◽  
Author(s):  
Pratesh Jayaswal ◽  
S.N. Verma ◽  
A.K. Wadhwani
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Vibration Signal Analysis

Research on the Gear Box Fault Diagnosis of Self-Propelled Artillery Based on Vibration Signal Analysis

2014 ◽  
Vol 940 ◽  
pp. 136-139
Author(s):  
Ren Bin Zhou ◽  
Yong Feng Zhang ◽  
Jie Min Yang ◽  
Feng Ling
Keyword(s):  
Fault Diagnosis ◽  
Data Acquisition ◽  
Real Time ◽  
Signal Analysis ◽  
Great Influence ◽  
Vibration Signal ◽  
Vibration Data ◽  
Gearbox Vibration ◽  
Gear Box ◽  
Vibration Signal Analysis

As a universal component connection and power transmission gear box, is widely used in the modern industrial equipment, but also an easy failure parts, has a great influence on the running state of the working performance of the whole machine. This paper first analyzes the gear box fault form and characteristics, the gear box fault diagnosis method based on vibration signal analysis, and analysis of the vibration signal processing method for gear vibration signal analysis in time domain, including parameters, resonance demodulation method and cepstrum analysis method. Then using Visual C + + language and data acquisition card for real-time acquisition of gearbox vibration data software, including parameter setting, data acquisition module, signal real-time display module and data storage module. The data acquisition program is developed, the actual acquisition of gearbox vibration data of gear fault and bearing fault, and analyzed.

The Use of Blind-Source-Separation Algorithm for Mechanical Signal Separation and Machine Fault Diagnosis

2003 ◽  
Author(s):  
Peter W. Tse ◽  
Jinyu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Condition Monitoring ◽  
Source Separation ◽  
Simulated Data ◽  
Vibration Signal ◽  
Advantages And Disadvantages ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Mechanical Components

Vibration based machine fault diagnosis is widely adopted in machine condition monitoring. Since a machine is usually composed of many mechanical components, during the machine running, each component will generate its vibration and transmit to other components thru the shaft or linkages. Hence, the vibration signal collected from a sensor is the aggregation of all generated vibrations. To enhance the accuracy in vibration based machine fault diagnosis, the vibration generated by each component must be isolated and identified. In this paper, the performance of blind-source-separation (BSS) in separating various mixed sources is discussed. The BSS based method of second order statistics (SOS) has been applied to separate the aggregated vibration signals generated from a number of mechanical components. To verify the effectiveness of the BSS based SOS, a number of experiments were conducted using both simulated data and vibration generated form the industrial machines. The results show that the BSS possesses the ability to separate both artificially and naturally mixed signals. Such ability is definitely welcome in the fields of condition monitoring and maintenance. Moreover, the paper also discusses the advantages and disadvantages of the algorithm in the applications of machine fault diagnosis and future improvements.

Novel predictive features using a wrapper model for rolling bearing fault diagnosis based on vibration signal analysis

2020 ◽  
Vol 106 (7-8) ◽  
pp. 3409-3435 ◽  
Author(s):  
Issam Attoui ◽  
Brahim Oudjani ◽  
Nadir Boutasseta ◽  
Nadir Fergani ◽  
Mohammed-Salah Bouakkaz ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Vibration Signal Analysis

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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