Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same author

Grapevine LysM-containing receptor-like kinase <i>VvLYK6</i> enhances resistance to white rot in tomato.

Frontiers in plant science·2026

Same author

PaEDNet: A Robust Denoising and Classification Framework for Vibration-Based Fault Diagnosis with Measurement Noise.

Sensors (Basel, Switzerland)·2026

Same author

Protein language models for structural biology.

Nature computational science·2026

Same author

Editorial overview: Artificial intelligence methodologies in structural biology: From static snapshots to dynamic and interaction landscapes.

Current opinion in structural biology·2026

Same author

Pharmacokinetics of Nirmatrelvir/Ritonavir after Multiple Administration in a Phase 1, Open-Label Study in Healthy Chinese Adults and Compared with Non-Chinese Participants.

Clinical drug investigation·2026

Same author

Chip-scale high spectral purity micro-ring photon-pair source based on dual-pulse control.

Optics express·2026

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 20, 2025

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

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

Published on: January 5, 2024

Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network.

Dongyang Li^1,2, Jianyi Yang¹, Zaisheng Pan³

¹College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310013, China.

Sensors (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study introduces a new method using signal demodulation and convolutional neural networks (CNNs) to accurately identify elevator traction machine operating status. The approach significantly improves diagnostic accuracy compared to conventional techniques.

Keywords:

Fourier transform convolution neural network machinery industry status identification traction machine

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Related Experiment Videos

Last Updated: Jul 20, 2025

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

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

Published on: January 5, 2024

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Area of Science:

Mechanical Engineering
Signal Processing
Artificial Intelligence

Background:

Accurate identification of elevator traction machine operating status is crucial for safety and maintenance.
Existing methods struggle with subtle time-frequency signal differences, leading to low recognition accuracy.
Vibration signal analysis is key, but feature extraction under various conditions remains challenging.

Purpose of the Study:

To propose a novel method for enhancing the identification accuracy of elevator traction machine operating status.
To address the limitations of conventional methods in extracting features from complex vibration signals.
To develop an automated system for real-time monitoring of elevator traction machine performance.

Main Methods:

A signal demodulation method based on time-frequency analysis and principal component analysis (DPCA) was employed.
DPCA was used to extract modulation features from experimentally measured vibration signals.
A convolutional neural network (CNN) was utilized for feature vector extraction and automated state recognition.

Main Results:

The proposed method effectively extracted feature parameters across different operating states.
Diagnostic accuracy reached 96.94%, an improvement of approximately 16.61% over conventional methods.
The CNN model achieved reliable automatic recognition of the running state.

Conclusions:

The novel DPCA and CNN-based method offers a feasible and effective solution for identifying elevator traction machine operating status.
This approach significantly enhances diagnostic accuracy and overcomes limitations of traditional techniques.
The findings contribute to improved safety and predictive maintenance strategies for elevators.