Finite element analysis of vessels to study changes in natural frequencies due to cracks

Choubey, Anupam; Sehgal, D. K.; Tandon, N.

doi:10.1016/j.ijpvp.2006.01.001

Cited by 25 publications

(13 citation statements)

References 11 publications

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“…According to Choubey, Sehgal, and Tandon (2006), the Multilayer Perceptron (MLP) is the neural network model type most commonly described in the literature. An MLP is a type of supervised network as it needs a desired output in order to learn.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Egeth¹,

Klein²,

Schmorrow³

2014

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Egeth¹,

Klein²,

Schmorrow³

2014

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“…The value in natural frequency shifts can reach from tens of Hz to a few Hz [3,4]. Examination of the change in natural frequencies allows an estimation of both the location and size of the crack [5]. Using the fast Fourier transform (FFT) spectral resolution is fixed as an inverse of the duration of the recorded signal [6,7].…”

Section: Introductionmentioning

confidence: 99%

Improvement in accuracy of natural frequency determination based on the envelope of cross-correlation function

Kotowski¹

2016

ZN PRz Mechanika

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This paper presents a method of improvement of the accuracy in natural frequency determination when having impulse responses from impact testing. A new method is used for obtaining impulse response spectrum. The improvement in natural frequency determination is a result of improving the spectral resolution. For this, the new method uses calculation of surface area under the envelope of the cross-correlation function. This process is repeated by singleharmonic signal generated step-by-step with frequency changed iteratively. Thus the frequency resolution of determined spectrum is independent of length of analysed impulse response.

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“…Choubey et al (2006) used a finite element model to obtain the dynamic characteristics of intact and damaged vessels for the first eight modes of these structures. These characteristics were used as the input pattern of an ANN (artificial neural network) obtaining as output the size of the crack in the vessel.…”

Section: Introductionmentioning

confidence: 99%

Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing

Santos

Sousa

Silva

et al. 2014

Braz. J. Chem. Eng.

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-Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm) under 4 kgf/cm² of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages.

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Finite element analysis of vessels to study changes in natural frequencies due to cracks

Cited by 25 publications

References 11 publications

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Improvement in accuracy of natural frequency determination based on the envelope of cross-correlation function

Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing

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