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.
Hydromachinery is vital for clean and sustainable power generation, where reliable and efficient operation directly supports the stability of hydropower plants. To achieve this, real-time performance tracking and fault monitoring are becoming increasingly important. This review summarizes recent techniques and technolo...
Juhi Padma, Hemant J. Sagar· IOP Conference Series: Earth...· 0 citations
A hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings is proposed by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach, offering a reliable and scalable solution for predictive maint...
Gustavo Gomes Do Valle, Benjamin Soudhan, Meisam Mahdavi et al.· IEEE Access· 0 citations
Under complex operating conditions involving multiple faults, load variations, speed variations, and noise interference, rolling bearing vibration signals show strong non-stationary and nonlinear characteristics. Traditional feature extraction methods often have difficulty in representing complex fault information effe...
Tian-Chi Li, Yi-Min Zhang, Shu-Zhi Gao et al.· Transactions of the Canadian...· 0 citations
The hot strip mill is a typical large‐scale plant consisting of numerous units, where equipment failures may lead to significant downtime and product quality deterioration. To support stable operation, monitoring and diagnostic technologies with high interpretability are increasingly required. This paper proposes a m...
Toshihiro Nii, Ryo Saito, Shǒ Itǒ et al.· IEEJ Transactions on Electro...· 0 citations
Reliable fault detection and diagnosis in wind turbine systems remains a significant challenge due to the highly dynamic and uncertain nature of real-world operating environments. Variations in wind speed, turbulence intensity, mechanical loading, and sensor noise can substantially degrade the performance of convention...
Imen Nakti, Majdi Mansouri, A. Kouadri et al.· IEEE Open Journal of the Ind...· 0 citations
This study investigates the consistency and impact of feature selection methods on deterministic and probabilistic normal behaviour models (NBMs) for estimating rear generator bearing temperature in wind farms using SCADA data. Rigorous NBM development enables the transfer of key features, enhances anomaly detection...
Daragh O'Connnor, V. Pakrashi, Bidisha Ghosh· Wind Energy· 0 citations
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