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Condition monitoring has become a critical technique for enabling predictive maintenance in continuous process industries. This technique holds significant potential in sectors such as thermal power plants, chemical processing, and industries like steel, glass, and sugar manufacturing. It plays a crucial role in evaluating equipment health and proactively diagnosing the root causes of any abnormalities. By implementing these techniques, maintenance costs and downtime are significantly reduced, leading to optimal plant capacity utilization. The present study encompasses a comprehensive review of literature, case studies, and field investigations of various online and offline condition monitoring methods currently in use at the 250 × 2 MW Adani Dahanu Thermal Power Station. These methods include vibration analysis, noise monitoring, oil condition monitoring, ultrasonic testing, motor current signature analysis, and more, applied to critical plant equipment and auxiliaries such as Boiler Feed Water Pumps, Primary Air and Forced Draught fans, turbines, motors, and conveyors. The field study at Adani Dahanu Thermal Power station revealed that vibration analysis on the Primary Air fans yielded acceptable results, with velocity readings conforming to ISO 10816-1 standards and acceleration values between 0.3 and 0.4 G. Additionally, this work outlines the design of a proposed condition monitoring system leveraging IoT technology for a single-phase induction or synchronous motor, detailing the methodology and simulation test results for its implementation.

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Condition Monitoring—An Emerging Predictive Maintenance Technique in Continuous Process Plants

  • 会议论文

  • 发表日期：

    2026年

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  • Atharva D. Joshi,
  • Tanishq D. Kale,
  • Divyesh P. Jilka,
  • Aditya V. Thakkar,
  • Parag K. Muley

摘要

Condition monitoring has become a critical technique for enabling predictive maintenance in continuous process industries. This technique holds significant potential in sectors such as thermal power plants, chemical processing, and industries like steel, glass, and sugar manufacturing. It plays a crucial role in evaluating equipment health and proactively diagnosing the root causes of any abnormalities. By implementing these techniques, maintenance costs and downtime are significantly reduced, leading to optimal plant capacity utilization. The present study encompasses a comprehensive review of literature, case studies, and field investigations of various online and offline condition monitoring methods currently in use at the 250 × 2 MW Adani Dahanu Thermal Power Station. These methods include vibration analysis, noise monitoring, oil condition monitoring, ultrasonic testing, motor current signature analysis, and more, applied to critical plant equipment and auxiliaries such as Boiler Feed Water Pumps, Primary Air and Forced Draught fans, turbines, motors, and conveyors. The field study at Adani Dahanu Thermal Power station revealed that vibration analysis on the Primary Air fans yielded acceptable results, with velocity readings conforming to ISO 10816-1 standards and acceleration values between 0.3 and 0.4 G. Additionally, this work outlines the design of a proposed condition monitoring system leveraging IoT technology for a single-phase induction or synchronous motor, detailing the methodology and simulation test results for its implementation.