Figure 1: A schematic of an asynchronous machine highlighting its main components like
rotor, stator, and bearings
Time-domain Analysis: Before delving into complex algorithms, simple statistical measures
from the time domain offer valuable insights. Measures like RMS, peak value, and kurtosis serve as
primary features for fault identification.
Figure 2: Graph showing time-domain vibration signals and highlighting statistical
measures
Frequency-domain Analysis (FFT) :
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Fast Fourier Transform translates vibration signals from time to frequency domain, elucidating
specific frequency components related to known faults.
Figure 3: FFT spectrum of a healthy machine vs. one with a rotor fault
Envelope Analysis & Bearing Faults:
Bearings are crucial components, and their faults can be catastrophic. Envelope analysis
demodulates the vibration signal to pinpoint bearing fault frequencies.
Figure 4: Envelope spectrum highlighting bearing defect frequencies
Advanced Techniques - Wavelet & Machine Learning :
Wavelet transform decomposes signals into different frequency bands, apt for transient fault
detection. With features extracted, machine learning models, such as SVM and Neural Networks,
classify fault patterns efficiently.
Figure 5: A wavelet decomposition of a vibration signal
Deep Learning Approach: Deep learning, a subset of machine learning, employs architectures
like Convolutional Neural Networks (CNNs) to analyze raw vibration data. This approach is adept at
capturing intricate fault patterns, often missed by traditional methods.
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