title: Figure 1: A schematic of an asynchronous machine highlighting its main components like
text: 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) :
  Namangan Institute of Engineering and Technology
  nammti.uz
  10.25.2023
  Pg.403
  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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