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This work presents the design and development of an Intelligent Lift Safety and Predictive Maintenance System to improve elevator reliability, performance, and safety. It comprises an STM32F446RE microcontroller, temperature, vibration, and load sensors, Human–Machine Interface (HMI) display for real-time feedback, and an ESP8266 Wi-Fi module for cloud connectivity. Unlike conventional post-failure inspection-based lift systems, this novel design realizes real-time anomaly detection, automated safety notice, and predictive maintenance scheduling through continuous sensor data logging and cloud connectivity. Experimental validation is demonstrated under ±1.5 °C temperature detection accuracy, cloud update delay <3 s, and stable wireless connectivity (<95% uptime). These results demonstrate the feasibility of an embedded IoT-based lift safety platform and offer the potential for future extension to AI-based diagnostic capability, autonomous emergency halts, and mobile notification services.

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Design and Implementation of an Intelligent Lift Safety and Predictive Maintenance System

  • 会议论文

  • 发表日期：

    2026年

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  • Ashwini Barbadekar,
  • Aparna Barbadekar,
  • Abhay Chopde,
  • Atharva Namdev Chavan,
  • Amit Barde,
  • Sapna Wagaj

摘要

This work presents the design and development of an Intelligent Lift Safety and Predictive Maintenance System to improve elevator reliability, performance, and safety. It comprises an STM32F446RE microcontroller, temperature, vibration, and load sensors, Human–Machine Interface (HMI) display for real-time feedback, and an ESP8266 Wi-Fi module for cloud connectivity. Unlike conventional post-failure inspection-based lift systems, this novel design realizes real-time anomaly detection, automated safety notice, and predictive maintenance scheduling through continuous sensor data logging and cloud connectivity. Experimental validation is demonstrated under ±1.5 °C temperature detection accuracy, cloud update delay <3 s, and stable wireless connectivity (<95% uptime). These results demonstrate the feasibility of an embedded IoT-based lift safety platform and offer the potential for future extension to AI-based diagnostic capability, autonomous emergency halts, and mobile notification services.