Proactive detection of faults in turbine bearings is key to ensuring system reliability in industrial systems. This work introduces the use of Moving Average (MA) and Exponentially Weighted Moving Average (EWMA) control charts for diagnosing bearing faults in the context of predictive maintenance. Unlike other statistical control charts, MA and EWMA control charts provide an advantage in that they employ a dynamic method of identifying trends over time through smoothing out variations as well as quickening the detection of gradual trends in system performance. MA charts are well-suited to find mean behavioral trends since they offer a strong perspective of medium-term behavioral changes. EWMA diagrams show better accuracy in spotting minor, slow variations, which is especially helpful for early-stage fault detection in high-sensitivity settings like turbine bearings. Implementation of the proposed method for real-world turbine operating conditions is shown to demonstrate the potential of using MA and EWMA control charts in monitoring vibration as well as speed anomalies prior to and after maintenance. Results are presented as proof of efficacy in identifying faults, aiding in decision-making on maintenance, and improving the lifespan and system operability of turbomachinery.
Khouilid et al. (Wed,) studied this question.