Scholarlink

Purpose <p>With the rapid development of large-scale urbanization and the widespread adoption of high-rise buildings, elevators have become a critical component of modern infrastructure. Ensuring operational safety, enabling efficient fault diagnosis, and implementing predictive maintenance are key challenges in the transition toward intelligent elevator systems. This paper aims to analyze these challenges and provide insights into advancing elevator safety and intelligence.</p> Method <p>This paper begins by identifying key elevator components prone to faults and analyzing their failure characteristics. It then reviews current research on elevator fault diagnosis and prediction, covering the application scope, operating conditions, and effectiveness of various diagnostic models. Additionally, the paper examines methods for collecting elevator fault data and highlights recent advancements in sensor-based fault detection for different elevator components.</p> Results <p>Research indicates that elevator safety and reliability depend on effective fault detection, predictive maintenance, and human-factor risk mitigation. However, the lack of publicly available benchmark datasets and comprehensive safety assessment models remains a significant barrier. This paper synthesizes existing approaches and identifies gaps in current methodologies, particularly in addressing safety failures caused by human factors.</p> Conclusion <p>By consolidating current research and outlining future directions, this paper emphasizes the need for a holistic safety assessment framework, standardized datasets, and improved warning systems for human-factor-related failures.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Review of Fault Modes,Diagnosis and Prediction Methods for Elevator Systems

  • 期刊论文

  • 发表日期：

    2025年8月15日

查看全文
  • Cheng Zhenbo,
  • Yuan Mu,
  • Zhang Haoxin,
  • Xu Xuesong,
  • Liu Wenchao,
  • She Kun,
  • Qiu Bin,
  • Zhang Yuanming,
  • Zhang Zhenjie,
  • Xiao Gang

摘要

Purpose

With the rapid development of large-scale urbanization and the widespread adoption of high-rise buildings, elevators have become a critical component of modern infrastructure. Ensuring operational safety, enabling efficient fault diagnosis, and implementing predictive maintenance are key challenges in the transition toward intelligent elevator systems. This paper aims to analyze these challenges and provide insights into advancing elevator safety and intelligence.

Method

This paper begins by identifying key elevator components prone to faults and analyzing their failure characteristics. It then reviews current research on elevator fault diagnosis and prediction, covering the application scope, operating conditions, and effectiveness of various diagnostic models. Additionally, the paper examines methods for collecting elevator fault data and highlights recent advancements in sensor-based fault detection for different elevator components.

Results

Research indicates that elevator safety and reliability depend on effective fault detection, predictive maintenance, and human-factor risk mitigation. However, the lack of publicly available benchmark datasets and comprehensive safety assessment models remains a significant barrier. This paper synthesizes existing approaches and identifies gaps in current methodologies, particularly in addressing safety failures caused by human factors.

Conclusion

By consolidating current research and outlining future directions, this paper emphasizes the need for a holistic safety assessment framework, standardized datasets, and improved warning systems for human-factor-related failures.