Abstract:
Detecting cracks in rotating shafts is crucial for ensuring the reliability and
safety of various mechanical systems. This final year project presents a novel
approach to crack detection in rotating shafts using advanced signal
processing techniques and machine learning algorithms. The proposed
method leverages vibration signals obtained from sensors attached to the
shaft to identify and localize the presence of cracks.
Initially, the project involves the acquisition of vibration data from a
laboratory-scale test rig simulating real-world operating conditions. Signal
processing techniques such as Fourier analysis and wavelet transform are
applied to extract relevant features from the vibration signals. These features
are then utilized to train machine learning models, including support vector
machines (SVM) and artificial neural networks (ANN), for crack detection.
The effectiveness of the developed crack detection system is evaluated
through extensive experimental validation on the test rig. The results
demonstrate the capability of the proposed method to accurately detect and
localize cracks in rotating shafts, even in the presence of noise and varying
operating conditions. Furthermore, the project explores the integration of the
crack detection system into existing condition monitoring frameworks for
proactive maintenance of industrial machinery.
Overall, this final year project contributes to the advancement of fault
diagnosis techniques for rotating machinery, with potential applications in
industries such as manufacturing, power generation, and transportation.
