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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Bearing stress refers to the contact pressure between two separate bodies. To visualize this, imagine a bolt thrust through a plate. The bolt applies a force to the plate, which exerts an equal but opposite force back onto the bolt. This force isn't just a singular entity but a compilation of numerous smaller forces distributed across the contact surface between the bolt and the plate.
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The shaft PQ is subjected to a twisting force when equal and opposite torques are applied on either side. A section that cuts perpendicular to the shaft's axis at any arbitrary point R is examined to understand this. When the free-body diagram of the QR segment is analyzed, it reveals the shearing forces exerted by the PR portion onto the QR segment as the shaft experiences twisting.
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Understanding the calculations and concepts related to double-collar bearings is essential for engineers and designers to optimize the performance of these components in various applications. By analyzing the bearing under different conditions, one can ensure that it can withstand the forces and moments experienced during operation. This knowledge enables better decision-making when designing and selecting bearings for specific purposes and configurations. Consider a double-collar bearing with...
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Low-cost prototype for bearing failure detection using Tiny ML through vibration analysis.

Andres Felipe Cotrino Herrera1, Jesús Alfonso López Sotelo1, Juan Carlos Blandón Andrade2

  • 1School of Engineering and Basic Sciences, Universidad Autónoma de Occidente, Cali, Colombia.

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Summary
This summary is machine-generated.

This study introduces a low-cost, open-source device for learning Artificial Intelligence (AI) in embedded systems by analyzing bearing vibrations. It enables students to classify defective bearings, enhancing STEM skills and promoting TinyML applications.

Keywords:
Artificial intelligenceMachine learningTeaching strategyVibration analysis

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Area of Science:

  • Engineering Education
  • Embedded Systems
  • Artificial Intelligence

Background:

  • Traditional engineering education often lacks hands-on experience with industrial challenges.
  • Integrating practical applications like vibration analysis and AI is crucial for modern STEM learning.
  • The need for accessible, low-cost tools to teach complex technologies like Artificial Intelligence (AI) and Tiny Machine Learning (TinyML) is growing.

Purpose of the Study:

  • To present a low-cost, open-source device for learning AI in embedded systems using vibration analysis.
  • To enhance STEM education by incorporating industrial challenges and practical AI applications.
  • To enable students to classify bearing defects using AI through a hands-on prototype.

Main Methods:

  • The study details the design and construction of a device integrating electronic, mechanical, and software components.
  • Vibration data from bearings is collected and analyzed.
  • Artificial Intelligence (AI) models are developed and implemented on the device for defect classification.

Main Results:

  • The developed device successfully facilitates learning of AI in embedded systems through vibration analysis.
  • The prototype allows for the classification of defective bearings using AI.
  • The project provides a replicable model for integrating industrial challenges into educational settings.

Conclusions:

  • The device fosters STEM skills and promotes the application of AI and TinyML in real-world contexts.
  • It enriches educational programs by encouraging interdisciplinary learning and hands-on experimentation.
  • The open-source nature of the project allows for widespread adoption and further development in engineering education.