Artificial Neural Network and Finite Element Investigation of Cracked Rotor Shaft
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Abstract
The rotary shaft is an important machine part. Due to cyclic loading and unloading fatigue cracks may appear in the rotary shaft. Crack generated in the rotary shaft causes sudden failure of machine parts. The presence of crack varies the dynamic properties of rotary shaft system such as an amplitude of vibration, natural frequency of vibration, and critical speed. In the present experiment, an experimental set up generated to identify the effect of crack severity on critical speed. Fast Fourier Transform set up used to plot frequency amplitude graph by varying crack depth. A model of a shaft with a single crack generated using 3D modelling software. Models with varying crack depth developed. Modal analysis of shaft models carried out to get natural frequencies of vibration. Variations in natural frequencies of shaft observed with respect to Severity of crack. Natural frequency and critical speed data used to identify crack location and severity using artificial neural network.
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