This dissertation proposes hybrid models for (i) diagnosis and (ii) remaining useful life estimation of a single fatigue crack in a low-pressure turbine blade. The proposed hybrid methods consist of physics-based methods and data-driven methods. In this dissertation, blade tip timing is used to measure the relative tip displacement of a rotor blade. The natural frequency of the blade is determined by detecting the critical speeds of the blade using a newly derived least squares spectral analysis method. The method shares its origin from the Lomb-Scargle periodogram and can detect resonance frequencies in the blade’s displacement while the rotor is in operation. A Campbell diagram is then used to convert the critical speed into a natural frequency. Two kinds of shaft transients are considered, a run-up run-down crossing the same critical speed, is used to test the new method. This dissertation shows that the relative displacement of the blade tip is comparable to those simulated from an analytical single degree of freedom model. It is also shown that the newly proposed resonance detection method estimates the natural frequency of the blade to a high degree of accuracy when compared to the measurements from a modal impact hammer test. The natural frequency obtained from the real time measurement is then used in a pre-constructed hybrid diagnostics model. The diagnostics model provides a probability density function estimation of the surface crack length given the measured natural frequency. A Gaussian Process Regression model is trained on data collected during experiments and finite element simulations of a fatigue crack in the blade. The final part of this dissertation is a sequential inference model for improving the estimation of the crack length and the prediction of the crack growth. The suggested model uses an unscented Kalman filter that improves estimations of the crack length and the rate of crack growth from Paris’ Law coefficients. The model is updated each time a diagnosis is performed on the blade. The RUL of the blade is then determined from an integration of Paris’s Law given the uncertainty estimates of the current damage in the blade. The result of the algorithm is an estimation of the remaining number of cycles to failure. The algorithm is shown to improve the overall estimation of the RUL; however, it is suggested that future work looks at the convergence rate of the method. / Dissertation (MEng)--University of Pretoria, 2019. / Eskom Power Plant Engineering Institute (EPPEI) / Mechanical and Aeronautical Engineering / MEng / Unrestricted

The reliable operation of planetary gearboxes is critical for the sustained operation of many machines such as wind turbines and helicopter transmissions. Hybrid methods that make use of the respective advantages of physics-based and data-driven models can be valuable in addressing the unique challenges associated with the condition monitoring of planetary gearboxes. In this dissertation, a hybrid framework for diagnostics and prognostics of planetary gearboxes is proposed. The proposed framework aims to diagnose and predict the root crack length in a planet gear tooth from accelerometer measurements. Physics-based and data-driven models are combined to exploit their respective advantages, and it is assumed that no failure data is available for training these models. Components required for the implementation of the proposed framework are studied separately and challenges associated with each component are discussed. The proposed hybrid framework comprises a health state estimation and health state prediction part. In the health state estimation part of the proposed framework, the crack length is diagnosed from the measured vibration response. To do this, the following model components are implemented: A first finite element model is used to simulate the crack growth path in the planet gear tooth. Thereafter, a second finite element model is used to establish a relationship between the gearbox time varying mesh stiffness, and the crack length in the planet gear tooth. A lumped mass model is then used to model the vibration response of the gearbox housing subject to the gearbox time varying mesh stiffness excitation. The measurements from an accelerometer mounted on the gearbox housing are processed by computing the synchronous average. Finally, these model components are combined with an additional data-driven model for diagnosing the crack length from the measured vibration response through the solution of an inverse problem. After the crack length is diagnosed through the health state estimation model, the Paris crack propagation law and Bayesian state estimation techniques are used to predict the remaining useful life of the gearbox. To validate the proposed hybrid framework, an experimental setup is developed. The experimental setup allows for the measurement of the vibration response of a planetary gearbox with different tooth root crack lengths in the planet gear. However, challenges in reliably detecting the damage in the experimental setup lead to the use of simulated data for studying the respective components of the hybrid method. Studies conducted using simulated data highlighted interesting challenges that need to be overcome before a hybrid diagnostics and prognostics framework for planetary gearboxes can be applied in practice. / Dissertation (MSc)--University of Pretoria, 2021. / Eskom EPPEI / Mechanical and Aeronautical Engineering / Msc / Unrestricted

No description available.