Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

2.9K
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...
2.9K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.3K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.3K
Vibrating Concrete01:19

Vibrating Concrete

219
Mechanical vibrators are instrumental in compacting newly poured concrete within formwork and around reinforcements. This process is essential to eliminate trapped air pockets and establish a dense concrete mass. One widely used method is vibrating by internal vibrators, often referred to as a poker vibrator or immersion vibrator. It is rapidly inserted through the full depth of the freshly laid concrete and slightly extends into the layer below it (which remains in a plastic state). Consistent...
219
Equilibrium and Balance01:15

Equilibrium and Balance

5.2K
The inner ear assumes dual functionalities of auditory perception and equilibrium maintenance. The vestibule is the organ responsible for balance. This organ contains mechanoreceptors, specifically hair cells, endowed with stereocilia, which aid in deciphering information regarding the position and motion of our heads. Two intrinsic components, the utricle and saccule, help perceive head position, while the semicircular canals track head movement. Neurological messages initiated in the...
5.2K
Stress Concentrations in Circular Shafts01:18

Stress Concentrations in Circular Shafts

282
Consider the elastic torsion formula, which applies to a circular shaft with a consistent cross-section. This formula assumes that the shaft's ends are loaded with rigid plates firmly attached. However, in many cases, torques are applied to the shaft through mechanisms like flange couplings or gears, which are connected by keys inserted into keyways. This application method modifies the stress distribution near the point of torque application, causing it to deviate from the distributions...
282
IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

1.9K
A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
1.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Scan Path Optimization and YOLO-Based Detection for Defect Inspection of Curved and Glossy Surfaces.

Sensors (Basel, Switzerland)·2026
Same author

A Physics-Guided Dual-Sensor Framework for Bearing Fault Diagnosis in PMDC Motor Drives.

Sensors (Basel, Switzerland)·2026
Same author

Optimization of Flow Rate for Uniform Zinc Phosphate Coating on Steel Cylinders: A Study on Coating Uniformity and Elemental Composition Using Scanning Electron Microscopy (SEM).

Materials (Basel, Switzerland)·2025
Same author

Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems.

Sensors (Basel, Switzerland)·2025
Same author

Hyperelastic and Stacked Ensemble-Driven Predictive Modeling of PEMFC Gaskets Under Thermal and Chemical Aging.

Materials (Basel, Switzerland)·2024
Same author

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS).

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Oct 3, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K

Correlation Coefficient Based Optimal Vibration Sensor Placement and Number.

Geon-Ho Shin1, Jang-Wook Hur1

  • 1Mechanical Engineering (Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology, Gumi 39177, Korea.

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

This study presents a computationally efficient method for optimizing vibration sensor placement. By using correlation coefficients and Finite Element Analysis (FEA), it significantly reduces the number of sensors needed for accurate fault diagnosis.

Keywords:
automatic storagefinite element analysisfisher information matrixmodal massmode shape

More Related Videos

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects
07:32

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Published on: September 1, 2016

12.9K
A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli
07:28

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

Published on: August 2, 2016

7.4K

Related Experiment Videos

Last Updated: Oct 3, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K
Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects
07:32

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Published on: September 1, 2016

12.9K
A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli
07:28

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

Published on: August 2, 2016

7.4K

Area of Science:

  • Mechanical Engineering
  • Structural Health Monitoring
  • Signal Processing

Background:

  • Vibration sensors are crucial for machine and structural fault diagnosis.
  • Increasing sensor count enhances diagnostic accuracy but raises costs.
  • Optimizing sensor number and location is vital for efficient fault diagnosis.

Purpose of the Study:

  • To propose a practical and computationally efficient method for optimizing vibration sensor placement.
  • To reduce the computational burden associated with optimizing sensor locations for complex systems.
  • To enable accurate fault diagnosis with a minimized number of vibration sensors.

Main Methods:

  • Finite Element Analysis (FEA) to determine displacement values at various frequencies and candidate points.
  • Application of correlation coefficients to FEA results for optimizing sensor location and quantity.
  • Utilizing the Fisher Information Matrix (FIM) on reduced candidate points for further optimization.

Main Results:

  • Correlation coefficients effectively reduced 24,252 potential sensor points to just 10.
  • The proposed method significantly decreases computational throughput compared to existing approaches.
  • FIM became computationally feasible for optimization after initial reduction by correlation coefficients.

Conclusions:

  • A practical and computationally efficient approach for optimizing vibration sensor placement has been developed.
  • This method is suitable for large-scale or complex structures, offering a balance between accuracy and cost.
  • The study demonstrates a novel application of correlation coefficients and FIM for sensor optimization.