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Related Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

170
Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
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Fast Fourier Transform01:10

Fast Fourier Transform

447
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
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Fault Types01:18

Fault Types

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When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
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Related Experiment Video

Updated: Sep 1, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency

Mohammed Hakim1, Abdoulhadi A Borhana Omran2, Jawaid I Inayat-Hussain3

  • 1Department of Mechanical Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, Malaysia.

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a robust One-Dimensional Convolutional Neural Network (1D-CNN) for intelligent fault diagnosis, achieving high accuracy even with significant noise interference. The model demonstrates exceptional resilience and domain adaptation for reliable rolling bearing fault detection.

Keywords:
bearingdeep learningfast Fourier transformfault diagnosisone-dimensional convolutional neural networksignal-to-noise ratio

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Area of Science:

  • Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Intelligent fault diagnosis methods face challenges from environmental noise and limited degradation data.
  • Developing robust and straightforward diagnostic models is crucial for industrial applications.

Purpose of the Study:

  • To propose a facile and robust One-Dimensional Convolutional Neural Network (1D-CNN) model for intelligent fault diagnosis.
  • To enhance the model's resilience to noise and improve its domain adaptation capabilities.

Main Methods:

  • Utilized Fast Fourier Transform (FFT) for frequency-domain signal processing.
  • Employed phasor notation to represent signal magnitude and phase.
  • Trained the 1D-CNN model with White Gaussian Noise (WGN) for noise robustness.

Main Results:

  • Achieved 100% classification accuracy on clean signals.
  • Maintained high diagnostic accuracy (97.37%) even at a Signal-to-Noise Ratio (SNR) of -10 dB.
  • Demonstrated superior performance compared to existing models under various conditions and noise levels.

Conclusions:

  • The proposed 1D-CNN model offers a promising and effective solution for rolling bearing fault diagnosis.
  • The frequency-domain approach combined with noise training significantly enhances diagnostic robustness.
  • The model exhibits excellent domain adaptation and outperforms state-of-the-art methods.