Statistical Control Charts for Proactive Bearings Fault Diagnosis in Turbines: Advancing Predictive Maintenance in Renewable Energy Systems

The purpose of this study is to introduce an innovative method for diagnosing bearing issues in turbines using statistical control charts, with the aim of improving diagnostic abilities, leading to enhanced operational reliability and extended turbine lifespan.

The proposed method employs a data-driven strategy, utilizing statistical control charts to monitor crucial parameters linked to bearing health in real-time. This enables the detection of subtle deviations from typical operating conditions.

Experimental results demonstrate the value of statistical control charts in detecting and monitoring bearing faults, showcasing their potential to transform predictive maintenance in industrial machinery.

The method offers clear advantages over traditional approaches that depend on periodic inspections or subjective assessments, providing a systematic and proactive approach to early fault detection. By addressing the shortcomings of traditional inspection methods, the proposed method enables real-time tracking and early detection of faults, leading to improved operational reliability and extended turbine lifespan.