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.
Ali Khouilid, Erroumayssae Sabani, H. Mastouri et al.· Engineer· 0 citations
Monitoring the health of bearings in industrial rotating machines is a major challenge for ensuring the reliability and continuous operation of installations. Conventional fault detection methods, based on multivariate control charts such as Hotelling’s T2, multivariate exponentially weighted moving average, or multivariate cumulative sum control chart, are limited by the complex nonlinear dynamics of the system. In this article, we propose an innovative monitoring approach based on the Koopman operator, allowing the linearization of a nonlinear system in an observed space and the application of drift detection techniques via an extended T2 control chart. The study is based on two experimental approaches: one using controlled simulated data to analyze the responsiveness and robustness of the model, and the other applied to real data from an industrial turbogenerator monitoring the vibrations, temperatures, and speeds of the front and rear bearings. Comparative results show that the Koopman-based T2 map detects defects earlier, with better accuracy under noise and a reduced false alarm rate compared to conventional methods. The integration of wavelet preprocessing, statistical feature extraction by sliding windows, and PCA representation of the trajectories enhances the robustness and interpretability of the model.
Erroumayssae Sabani, E. Loualid, H. Mastouri et al.· Engineer· 0 citations