• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.15 no.11 s.104
• /
• pp.1232-1240
• /
• 2005

The purpose of this paper is that investigates the dynamic behavior characteristic of W.T.S (wind turbine system) and carries out the evaluation analysis during operating W.T.S. To investigate the dynamic behavior characteristic of W.T.S, the experiments to measure vibration of the blade from the attached accelerometer on the flap and edge section of the blade that is one of the most important elements of dynamic characteristic of W.T.S are performed. Natural frequency and mode shape are calculated with commercial program ( ANSYS) using the measured vibration acceleration that receives the signal with F.F.T Analyzer from the accelerometer For validation of these experiments, the finite element analysis is performed with commercial F.E.M program (ANSYS) on the basis of the natural frequency and mode shape. The results indicate that experimental values have good agreements with the finite element analysis.

• Smart Structures and Systems
• /
• v.20 no.6
• /
• pp.669-682
• /
• 2017

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.38 no.2
• /
• pp.205-210
• /
• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.