Condition Monitoring of High-Speed Rotating Machinery Using Digital Twins & Machine Learning Techniques

<div class="section abstract"><div class="htmlview paragraph">In the era of Industry 4.0, the maintenance of factory equipment is evolving with new systems using predictive or prescriptive methods. These methods leverage condition monitoring through digital twins, Artificial Intelligence, and machine learning techniques to detect early signs of faults, types of faults, locations of faults, etc. Bearings and gears are among the most common components, and cracking, misalignment, rubbing, and bowing are the most common failure modes in high-speed rotating machinery. In the present work, an end-to-end automated machine learning-based condition monitoring algorithm is developed for predicting and classifying internal gear and bearing faults using external vibration sensors. A digital twin model of the entire rotating system, consisting of the gears, bearings, shafts, and housing, was developed as a co-simulation between MSC ADAMS (dynamic simulation tool) and MATLAB (Mathematical tool). The gear and bearing models were developed mathematically, while the shaft and housing models were developed in dynamic environment as flexible bodies. The co-simulation was achieved through an S-function exported from dynamic environment, wherein the forces obtained mathematically were inputs to the dynamic environment, and displacements were treated vice versa. Autoregression and spectral kurtosis signal processing methods were implemented to perform denoising operations on raw vibration signals. Empirical mode decomposition (EMD) on vibration signals was performed for feature extraction studies. Subsequently, an artificial neural network (ANN) based machine learning model was trained for fault classification. Finally, the effectiveness of the proposed algorithm was verified by implementation on one case of rotating machinery under various operating level monitoring conditions.</div></div>