Scholarlink

<p>Induction motors (IM) are essential machines in various industrial applications due to their reliability and efficiency. The faults of stator winding (SW) can lead to severe performance issues if not detected early. This article presents a real-time condition monitoring and control system based on the Internet of Things (IoT), developed using low-cost components. The system utilizes an ESP32 microcontroller connected to voltage (ZMPT101B), current (SCT-013-030), temperature, and humidity (DHT22) sensors to predict voltage, current, humidity, and temperature, respectively. Transmitted data to the Blynk platform, users can remotely monitor motor parameters and control the IM under abnormal conditions. Artificial SW fault is created on slots 13 and 14 to identify different electrical parameters. The motor is tracked via the Internet of Things to avoid unexpected breakdowns. The experimental results demonstrated distinct parameter variations during fault instances, and the test motor (IM) was observed under both healthy and stator-faulty conditions. The motor is operating at a voltage of 125.28&#xa0;V, an increase in current to 0.635 A, humidity of 95.58, and a temperature raised to 67.08&#xa0;°C; the suggested work prevents rapid breakdown. These aberrant numbers indicate that the system can identify and notify operators of possible problems by acting as indications of the existence of a fault. Proactive maintenance is made quicker by the Internet of Things-based strategy, which diminishes the probability of unexpected malfunctions and minimizes downtime when combined with remote control instructions and real-time warnings. Reliable monitoring of induction motors is provided by this scalable and affordable monitoring solution, improving operational efficiency and safety. The proposed approach enhances automation with human oversight for sustainable industrial growth by decreasing the requirement for manual supervision and facilitating early problem detection, which is in line with Industry 5.0 application.</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

IoT-Driven Stator Winding Fault Analysis and Prevention for Induction Motor to Meet Industry 5.0 Application

  • 期刊论文

  • 发表日期：

    2025年8月10日

查看全文
  • Kapu V Sri Ram Prasad,
  • Salapu Ramya Sree,
  • Pyla Tanuja,
  • Thimadani Pujitha,
  • Neelapu Harsha Vardhani,
  • Gedela Jhansi

摘要

Induction motors (IM) are essential machines in various industrial applications due to their reliability and efficiency. The faults of stator winding (SW) can lead to severe performance issues if not detected early. This article presents a real-time condition monitoring and control system based on the Internet of Things (IoT), developed using low-cost components. The system utilizes an ESP32 microcontroller connected to voltage (ZMPT101B), current (SCT-013-030), temperature, and humidity (DHT22) sensors to predict voltage, current, humidity, and temperature, respectively. Transmitted data to the Blynk platform, users can remotely monitor motor parameters and control the IM under abnormal conditions. Artificial SW fault is created on slots 13 and 14 to identify different electrical parameters. The motor is tracked via the Internet of Things to avoid unexpected breakdowns. The experimental results demonstrated distinct parameter variations during fault instances, and the test motor (IM) was observed under both healthy and stator-faulty conditions. The motor is operating at a voltage of 125.28 V, an increase in current to 0.635 A, humidity of 95.58, and a temperature raised to 67.08 °C; the suggested work prevents rapid breakdown. These aberrant numbers indicate that the system can identify and notify operators of possible problems by acting as indications of the existence of a fault. Proactive maintenance is made quicker by the Internet of Things-based strategy, which diminishes the probability of unexpected malfunctions and minimizes downtime when combined with remote control instructions and real-time warnings. Reliable monitoring of induction motors is provided by this scalable and affordable monitoring solution, improving operational efficiency and safety. The proposed approach enhances automation with human oversight for sustainable industrial growth by decreasing the requirement for manual supervision and facilitating early problem detection, which is in line with Industry 5.0 application.