Review on Steam Turbine Alignment Monitoring System at Thermal Power Plant Koradi

Abstract

Steam turbines are critical components in power generation systems, requiring precise monitoring and control to ensure operational efficiency, reliability, and safety. The development of advanced Steam Turbine Monitoring Systems (STMS) integrates sensor technology, real-time data acquisition, signal processing, and intelligent diagnostic algorithms to detect performance anomalies, prevent failures, and optimize maintenance schedules. Recent studies highlight the application of vibration analysis, temperature and pressure monitoring, rotational speed tracking, and acoustic emission techniques as key indicators of turbine health. Integration with Industrial Internet of Things (IIoT) platforms and SCADA systems allows real-time remote monitoring, predictive maintenance, and data-driven decision-making. Artificial Intelligence (AI) and Machine Learning (ML) approaches are increasingly employed to analyze large datasets, predict fault conditions, and enhance turbine life-cycle management. Despite significant advances, challenges remain in terms of sensor accuracy, system integration, data security, and standardization. This review consolidates recent research trends, technological advancements, and practical implementations in STMS, identifying research gaps and future directions. The study emphasizes the potential of smart monitoring systems to improve turbine efficiency, reduce downtime, and enable sustainable energy generation, ultimately contributing to the reliability and profitability of power plants.

Introduction

Steam turbines are essential components of thermal power plants, converting steam energy into mechanical and electrical power. Because they operate under high temperature, pressure, and rotational speeds, they are vulnerable to failures such as rotor imbalance, blade erosion, vibration issues, lubrication faults, and steam leakage. Traditional periodic inspections cannot detect early-stage defects or dynamic variations, leading to costly breakdowns.

Modern Steam Turbine Monitoring Systems (STMS) use advanced sensors to measure vibration, temperature, pressure, rotational speed, and acoustic emissions in real time. With the integration of IIoT, SCADA, cloud computing, AI, and machine learning, these systems enable remote monitoring, predictive maintenance, and early fault diagnosis. AI-driven analytics can detect abnormal patterns, predict failures, and significantly reduce downtime and maintenance costs. Vibration monitoring remains one of the most effective diagnostic methods, while thermal and acoustic monitoring help detect leaks, structural anomalies, and efficiency losses.

Despite progress, challenges such as sensor accuracy, environmental noise, heterogeneous data integration, cybersecurity risks, and lack of standardized protocols still limit full implementation. Current research focuses on creating robust, scalable, intelligent monitoring frameworks to enhance turbine safety, reliability, and long-term performance.

The literature reveals that modern tools—laser alignment systems, vibration sensors, thermography, oil analysis, and IoT-enabled devices—greatly improve fault detection and reduce downtime. Studies emphasize combining multiple monitoring methods for comprehensive assessment. However, research gaps remain regarding practical implementation issues, cost–benefit analysis, and long-term performance in older plants such as Koradi Thermal Power Plant.

The study adopts a methodology involving multi-sensor data acquisition, SCADA/IIoT integration, noise filtering, statistical and AI-based analysis, and validation using historical data. Comparison of earlier and recent approaches shows a shift from manual and periodic inspection to automated, real-time, AI-based predictive monitoring.

Overall, the review highlights emerging trends such as real-time digital monitoring, predictive maintenance, AI-driven diagnostics, and cloud-based platforms. Key challenges include data noise, sensor calibration, cybersecurity, and integration with legacy systems. Addressing these issues will enable next-generation monitoring frameworks that improve turbine reliability, reduce operational risks, and enhance the efficiency and sustainability of thermal power plants.

Conclusion

Steam Turbine Monitoring Systems (STMS) play a pivotal role in enhancing the efficiency, reliability, and safety of power generation systems. Literature review indicates that real-time monitoring using multi-parameter sensors—covering vibration, temperature, pressure, and acoustic emissions—provides critical insights into turbine health, enabling early fault detection and proactive maintenance. Integration with SCADA, IIoT platforms, and cloud-based systems further enhances monitoring capabilities, while AI and Machine Learning techniques improve predictive diagnostics and decision-making. Despite these advancements, challenges such as sensor accuracy, data integration, noise filtering, cybersecurity, and standardization remain. The review highlights the need for intelligent, fully integrated monitoring systems capable of automated fault detection and predictive maintenance. Implementing such systems can significantly reduce downtime, maintenance costs, and operational risks, while extending turbine lifespan and supporting sustainable, reliable, and economically viable power generation.

References

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Copyright

Copyright © 2025 Ms. Bhavana C. Bhambarkar, Dr. Ravindra N. Dehankar, Dr. M. Shakebuddin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.