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 Experiment Video

Updated: Jun 27, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Physics-Informed Neural Network-Based Elevator Degradation Diagnosis and Early Warning.

Ren Li1,2, Gang Xiao1, Yuanming Zhang1

  • 1Zhejiang University of Technology, Gongshu District, Hangzhou 310014, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Impacts of cultivar, tissue type, and processing methods on sweet potato metabolome and antioxidant activity: integrating UHPLC-MS/MS, molecular networking, UV-Vis spectroscopy, and in vitro assays.

Food chemistry·2026
Same author

Evaluation of <i>Ganoderma lucidum</i> Across Varieties and Growth Stages: Integrating Chromatographic Profiling, Bioactivity Correlation, and In Silico Simulations.

Foods (Basel, Switzerland)·2026
Same author

High-Quality Nanopatterning of Oxide Interfaces Using Transferred Gold Masks.

ACS applied materials & interfaces·2026
Same author

A nomogram estimates postoperative urinary tract infection risk after upper urinary tract stone surgery with double-J stents.

Scientific reports·2026
Same author

Anti-fatigue mechanism of <i>Dendrobium officinale</i> pseudobulbs from different growth durations against chronic fatigue syndrome in mice and analysis of their active chemical components.

Frontiers in cell and developmental biology·2026
Same author

Discovery of phage lysin against Vibrio parahaemolyticus and characterization of its antibacterial activity.

International journal of food microbiology·2026

This study introduces a physics-informed neural network (PINN) for elevator health monitoring. The method enhances early degradation detection and reduces false alarms for predictive maintenance.

Area of Science:

  • Engineering
  • Artificial Intelligence
  • Predictive Maintenance

Background:

  • Urbanization increases elevator density, raising concerns about system reliability and maintenance.
  • Conventional monitoring methods struggle with complex conditions, noise, and detecting progressive degradation.

Purpose of the Study:

  • To develop an advanced method for elevator health monitoring and early warning using physics-informed neural networks (PINNs).
  • To improve the accuracy and reliability of degradation detection in elevator systems.

Main Methods:

  • Multi-sensor data processing with time alignment and feature reconstruction.
  • A dual-path acceleration estimation for stable dynamic state calculation.
  • Embedding a simplified elevator dynamic model into PINN for parameter identification.
Keywords:
degradation assessmentearly fault warningelevator health monitoringphysics-informed neural networkpredictive maintenance

Related Experiment Videos

Last Updated: Jun 27, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Constructing electrical and dynamic residual indicators for system condition assessment.
  • Implementing a time-accumulated risk model to detect progressive degradation.

    • Main Results:

    • Achieved stable parameter convergence and effective system condition assessment.
    • Demonstrated earlier detection of degradation trends compared to threshold-based methods.
    • Reduced false alarms caused by transient disturbances.

    Conclusions:

    • The PINN-based approach offers an interpretable and practical solution for elevator predictive maintenance.
    • This method enhances the intelligent operation and safety of elevator systems.
    • It provides a more robust alternative to conventional monitoring techniques.