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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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Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Self-help support groups are voluntary, community-based organizations that provide a platform for individuals with shared concerns to exchange support, insights, and practical strategies for coping with life challenges. Typically led by group members or paraprofessionals, these groups form a cornerstone of mental health care, especially in reaching populations that are underserved by traditional healthcare systems.
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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Updated: Jan 27, 2026

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Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes.

Rodrigo P Monteiro1, Mariela Cerrada2, Diego R Cabrera2

  • 1Federal University of Pernambuco, Recife 50740-550, Brazil.

Computational Intelligence and Neuroscience
|March 14, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to speed up deep learning for gearbox fault diagnosis. By using a decision stage, training time is reduced by 80% without losing accuracy.

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Gearboxes are critical mechanical components in applications like automotive transmissions.
  • Gearbox malfunctions can lead to significant economic losses and safety hazards.
  • Deep learning offers powerful solutions for gearbox fault diagnosis but requires substantial data and computational resources.

Purpose of the Study:

  • To reduce the training time of deep learning-based fault diagnosis systems for gearboxes.
  • To maintain or improve the accuracy of fault diagnosis despite reduced training time.
  • To address the challenge of training deep learning models when high-performance GPUs are unavailable.

Main Methods:

  • Implementation of a decision stage to interpret probability outputs from a classifier.
  • Utilizing a classifier with a softmax activation function in its output layer.
  • Application of two distinct classification algorithms for the decision-making process.

Main Results:

  • Achieved a reduction in training time by approximately 80%.
  • Maintained the average accuracy of the fault diagnosis system.
  • Demonstrated the feasibility of efficient deep learning model training.

Conclusions:

  • The proposed decision stage effectively reduces deep learning model training time for gearbox fault diagnosis.
  • The method offers a practical solution for scenarios with limited computational resources.
  • This approach enhances the accessibility and efficiency of AI-driven gearbox monitoring systems.