Analytical modelling prediction by using wake oscillator model for vortex-induced vibrations

Hussin, W.N.W.; Harun, Fatimah Noor; Mohd, M. H.; Rahman, Mohd Nordin Abdul

doi:10.15282/jmes.11.4.2017.14.0280

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…The validation were made succeeded previous coupling model by Hussin et al (2017) and comparison between analytical model by Facchinetti et al (2004) and experimental data from Govardhan and Williamson (2000) are shown in Figure 2. For comparison sake, the low mass ratio m^*=0.52 and low reduced damping ξ=0.0052 are used.…”

Section: Coupling Model Validationmentioning

confidence: 86%

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Modified wake oscillator model for vortex-induced motion prediction of low aspect ratio structures

Rahman

Hussin

Mohd

et al. 2019

Ships and Offshore Structures

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Modified wake oscillator model for vortex-induced motion prediction of low aspect ratio structures Floating structures, such as Spar platform, are likely to experience significant motions while operation due to Vortex-induced Motion (VIM). The main features of VIM include aspect ratio and mass ratio of the structure. This paper focuses on further development of a previously published semi-empirical method called wake oscillator model (WOM) for VIM, which aims to derive a mathematical correlation in modelling all features of the phenomenon. Analytical solution is proposed to coupled WOM and structure oscillator model based on the Van der Pol equation by integrating mass ratio, m* = 4.36, and various low aspect ratios between 0.3 and 2.0. Additionally, a new approach was initiated to embed several empirical parameters linked to the aspect ratio in the existing WOM. It was found the aspect ratio parameter in the modified WOM significantly affected the performance of the model for estimation of VIM on floating structures.

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Section: Coupling Model Validationmentioning

confidence: 86%

“…The angular frequency , derived from Equation 13, produced one or three positive real roots. Based on the solutions, angular frequency using coupling Equations (11) and 12…”

Section: Coupling Systemmentioning

confidence: 99%

Modified wake oscillator model for vortex-induced motion prediction of low aspect ratio structures

Rahman

Hussin

Mohd

et al. 2019

Ships and Offshore Structures

View full text Add to dashboard Cite

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“…In order to avoid any sort of equipment failures, it is very important to have a good prevention strategy. Vibration is a powerful tool that helps to provide a good information regarding the state of the assets either equipment or structure [4][5][6][7]. For machinery monitoring, vibration-based is one of the most popular techniques used in order to maintain and sustain industrial assets due to its simple, low-cost and easiness of implementation [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

Isham

Leong

Hee

et al. 2019

JMES

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Vibration-based monitoring and diagnosis provide an excellent and reliable monitoring strategies for maintaining and sustaining a million dollars of industrial assets. The signal processing method is one of the key elements in gearbox fault diagnosis for extracting most useful information from raw vibration signals. Variational mode decomposition (VMD) is one of the recent signal processing methods that helps to solve many limitations in traditional signal processing method. However, pre-determine the input parameters especially the mode number become a challenging task for using this method. Then, this study aims to propose an iterative approach for selecting the mode number for the VMD method by using the normalized mean value (NMV) plot. The NMV value is calculates based on the ratio of a summation of VMD modes and the input signals. The result shows that the proposed iterative VMD approach can select an accurate mode number for the VMD method. Then, the vibration signals decomposed into different VMD modes and used for gearbox fault diagnosis. Statistical features have been extracted from the selected VMD modes and pass into extreme learning machine (ELM) for fault classification. Iterative VMD-ELM provide significance improvement of about 20% higher accuracy in classification result as compared with EMD-ELM. Hence, this research study offers a new mean for gearbox diagnosis strategy.

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“…Formerly, in fault diagnosis, CNN is only applied to automatically select features from the generated vibration signal which is in onedimensional input of time domain. Vibration signals provide a useful information regarding the state of the assets either equipment or structure [7][8][9]. Although the direct usage of CNN on the generated vibration signals is widely used and step-less, only time data could be extracted whereas the significant spatial data of the signals which in this case is the sequence of frequency as stated by Janssens et al [10] could not be interpreted.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis employing Gabor and augmented architecture of convolutional neural network

Waziralilah

Abu

Lim

et al. 2019

JMES

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The vast impact on machinery that is rooted by bearing degradation thus pinpointing bearing fault diagnosis as indubitably very crucial. The research is innovated to diagnose the fault in bearing by implementing deep learning approach which is Convolutional Neural Network (CNN) that has superiority over image processing and pattern recognition. A novel model comprises of Gabor Transform and CNN is proposed whereby Gabor Transform is utilized in representing the raw vibration signals into its image representation. The CNN architecture is augmented for a better accuracy of the bearing fault diagnosis model. To date, the method combination has never been deployed in establishing fault diagnosis model. Plus, the usage of Gabor Transform in mechanical area especially in bearing fault diagnosis is meagrely reported. Scant researches in mechanical diagnosis are dedicated to work on the image representation of the vibration data whereas the CNN works better when fed by images input due to its unique strength of CNN in images processing and spatial awareness. At the end of the research, it is perceived that the proposed model comprises of Gabor Transform and CNN can diagnose the bearing faults with 100% accuracy and perform better than when CNN is fed with raw signals.

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Analytical modelling prediction by using wake oscillator model for vortex-induced vibrations

Cited by 8 publications

References 30 publications

Modified wake oscillator model for vortex-induced motion prediction of low aspect ratio structures

Modified wake oscillator model for vortex-induced motion prediction of low aspect ratio structures

Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

Bearing fault diagnosis employing Gabor and augmented architecture of convolutional neural network

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