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Related Concept Videos

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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When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Machines: Problem Solving I01:22

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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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Vibration-Based Loosening Detection of a Multi-Bolt Structure Using Machine Learning Algorithms.

Oybek Eraliev1, Kwang-Hee Lee2, Chul-Hee Lee2

  • 1Future Vehicle Engineering Department, Inha University, 100 Inharo, Mitchuholgu, Incheon 22212, Korea.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning (ML) technique for early bolt loosening detection in structural health monitoring (SHM). The method accurately identifies which bolt or bolts are loosening under various operating conditions.

Keywords:
bolt looseningbolt-loosening identificationloosening detectionmachine learningsignal processingvibration

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Area of Science:

  • Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Machine learning (ML) is increasingly utilized in structural health monitoring (SHM).
  • Early detection of bolt loosening is critical for structural integrity.

Purpose of the Study:

  • To propose a vibration-based technique for early-stage bolt loosening detection and identification using ML algorithms.
  • To evaluate the performance of different ML classifiers for this task.

Main Methods:

  • Established experimental cases with fastened and loosened bolts on a motor.
  • Collected vibration data under three different operating conditions (800, 1000, 1200 rpm).
  • Applied Short-Time Fourier Transform (STFT) for feature extraction and trained various ML classifiers.

Main Results:

  • The developed ML technique demonstrated satisfactory accuracy in detecting bolt loosening.
  • The system could identify specific bolts that began to lose preload across different working conditions.
  • The most accurate classifier was identified for practical implementation.

Conclusions:

  • The proposed vibration-based ML approach is effective for early bolt loosening detection and identification.
  • The findings support the implementation of this technique for online monitoring of multi-bolt structures.