Fault Analysis/Condition Monitoring of Vibrating Structures

Alkassar, Yassin (2016) Fault Analysis/Condition Monitoring of Vibrating Structures. MTech thesis.

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Structure Health monitoring (SHM) has considered a great deal of attention all over the world due to significant impact on safety and longevity of the structure. However, the presence of damage or fault like a crack in beam or plate structure for a long time increases the probability of system failure which leads to a loss of life and properties. In this research, two methods of SHM are studied in details. The first method based on Lamb wave. Where Structural Condition Monitoring based on guided Ultrasonic wave is becoming acquisition significance daily. Ultrasonic Guided Waves allow continuous controlling of structures and prevent them from a catastrophic failure. Since Lamb waves propagate for long distance and sensitive to many faults, they make them stand as power tool used for inspection in many applications. The implementation of Lamb waves in real structure demands a good knowledge of their propagation and interaction with discontinuous. In this research, a detailed study of the spread Lamb wave under different excitation frequencies in thin Aluminium plate is achieved using numerical simulations relied on finite element method and analytical solution. The isotropic plate is utilized to explain and analyse the basic features of Lamb wave mode. Furthermore, it observed that the numerical simulation results for group velocity of fundamental Lamb wave modes are shown to be in good agreement with the analytical solution. Appearing helpful of finite element method to modeling the Lamb wave propagation. After that; the interaction of selected Lamb wave mode with a notch in the plate is investigated in details. The existence the damage on the plate has been led to convert in Lamb wave modes. Which help us to assess the damage in the plate. The second method, in various techniques, vibration characteristic of structural component can be used as an influential tool for damage detection. In this research, modelling of transverse vibration of clamped beam with transverse crack has been studied to enable possible influence the crack parameters (relative crack position and relative crack depth) on relative natural frequencies based on measurement of natural frequency. Firstly, the variations in relative natural frequencies according to location and depth of a crack in cracked beam have been examined by developed finite element model. The suggested technique is based on vibration parameters such as natural frequencies and mode shapes. Secondly, the experimental set-up is accomplished for different damaged beams having a crack of various depths and locations to validate the FEA values. The results are to be close to each other. Finally, a controller based on the neural network has been developed to predict the crack parameters in structure (relative crack position and relative crack depth). The designed artificial neural network has been used Feed forward multi-layer as a type of neural network with backpropagation algorithm for training the neural network. Where the neural model is fed with relative natural frequencies as inputs and the outputs parameters are relative crack location and depth. Which are obtained from FEA after the results are validated by performing the experimental setup. The predicted results acquired from neural network model for crack identification are very close to experimental results therefore, the neural network can be used as intelligent tools for crack parameters detection in cracked clamped beam.

Item Type:Thesis (MTech)
Uncontrolled Keywords:Lamb Wave; Finite Element; Mode; Crack; Neural Network
Subjects:Engineering and Technology > Mechanical Engineering > Finite Element Analysis
Engineering and Technology > Mechanical Engineering > Structural Analysis
Divisions: Engineering and Technology > Department of Mechanical Engineering
ID Code:9187
Deposited By:Mr. Sanat Kumar Behera
Deposited On:06 May 2018 10:18
Last Modified:06 May 2018 10:18
Supervisor(s):Parhi, Dayal Ramakrushna

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