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Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

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Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Acceleration Vectors01:30

Acceleration Vectors

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In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
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Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

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When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
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Related Experiment Video

Updated: Jun 18, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle's Acceleration Measurements.

Masoud Haghbin1, Juan Chiachío1, Sergio Muñoz2

  • 1Department of Structural Mechanics and Hydraulic Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), 18001 Granada, Spain.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary

This study uses deep learning (CNN-1D) to predict rail corrugation from train movement data. The model accurately forecasts corrugation profiles, enabling proactive railway maintenance.

Keywords:
Grad-CAMconvolutional neural networksdeep learningrail corrugation

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Area of Science:

  • Railway Engineering
  • Machine Learning
  • Predictive Maintenance

Background:

  • Rail corrugation is a significant issue affecting railway infrastructure and operations.
  • Accurate prediction of rail corrugation is crucial for efficient maintenance strategies.

Purpose of the Study:

  • To develop and validate a deep learning model for predicting rail corrugation.
  • To assess the model's performance using on-board sensor data (vertical acceleration and forward velocity).

Main Methods:

  • Implementation of a One-Dimensional Convolutional Neural Network (CNN-1D).
  • Utilizing on-board rolling-stock vertical acceleration and forward velocity measurements.
  • Employing Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability.

Main Results:

  • The CNN-1D model achieved mean absolute percentage errors below 5% in predicting corrugation profiles.
  • The model demonstrated the ability to reproduce corrugation based on real-time sensor data.
  • Grad-CAM analysis confirmed the model's capability to identify different corrugation regions.

Conclusions:

  • Data-driven approaches like CNN-1D show significant potential for real-time rail corrugation prediction.
  • This predictive capability can enhance the reliability and efficiency of railway maintenance.
  • Online monitoring of rolling-stock dynamics offers a viable pathway for proactive infrastructure management.