Acoustic emission monitoring of propulsion systems : a laboratory study on a small gas turbine

The motivation of the work is to investigate a new, non-intrusive condition monitoring system for gas turbines with capabilities for earlier identification of any changes and the possibility of locating the source of the faults. This thesis documents experimental research conducted on a laboratory-scale gas turbine to assess the monitoring capabilities of Acoustic Emission (AE). In particular it focuses on understanding the AE behaviour of gas turbines under various normal and faulty running conditions. A series of tests was performed with the turbine running normally, either idling or with load. Two abnormal running configurations were also instrumented in which the impeller was either prevented from rotation or removed entirely. With the help of demodulated resonance analysis and an ANN it was possible to identify two types of AE; a background broadband source which is associated with gas flow and flow resistance, and a set of spectral frequency peaks which are associated with reverberation in the exhaust and coupling between the alternator and the turbine. A second series of experiments was carried out with an impeller which had been damaged by removal of the tips of some of the blades (two damaged blades and four damaged blades). The results show the potential capability of AE to identify gas turbine blade faults. The AE records showed two obvious indicators of blade faults, the first being that the energy in the AE signals becomes much higher and is distinctly periodic at higher speeds, and the second being the appearance of particular pulse patterns which can be characterized in the demodulated frequency domain

Fatigue-Life-Prediction-by-Means-of-Nonconstant-Variance-Probabilistic-Neural-Network

The modelling of fatigue using machine learning (ML) has been gaining traction in the engineering community. Among ML techniques, the use of probabilistic neural networks (PNNs) has recently emerged as a candidate for modelling fatigue applications. In this paper, we used PNNs with nonconstant variance to model fatigue. We present two case studies to demonstrate the approach. First, we model the fatigue life of cover-plated beams under constant amplitude loading and then we model the relationship between random vibration velocity and equivalent stress in process pipework. The two case studies demonstrate that PNNs can model the distribution of the data while also considering the variability of both distribution parameters (mean and standard deviation). This shows the potential of PNNs with nonconstant variance in modelling fatigue applications. All the data and code used in this paper will be available online.Financial support for this research was graciously provided by Qatar National Research Fund (a member of Qatar Foundation) via the National Priorities Research Project under grant NPRP-11S-1220-170112

Modelling Fatigue Uncertainty by Means of Nonconstant Variance Neural Networks

The modelling of fatigue using machine learning (ML) has been gaining traction in the engineering community. Among ML techniques, the use of probabilistic neural networks (PNNs) has recently emerged as a candidate for modelling fatigue applications. In this paper, we use PNNs with nonconstant variance to model fatigue. We present two case studies to demonstrate the developed approach. First, we model the fatigue life of cover-plated beams under constant amplitude loading, and then we model the relationship between random vibration velocity and equivalent stress in process pipework. The two case studies demonstrate that PNNs with nonconstant variance can model the distribution of the data while also considering the variability of both distribution parameters (mean and standard deviation). This shows the potential of PNNs with nonconstant variance in modelling fatigue applications. All the data and code used in this paper are openly available.Financial support for this research was graciously provided by Qatar National Research Fund (a member of Qatar Foundation) via the National Priorities Research Project under grant NPRP-11S-1220-170112. Open Access funding was graciously provided by the Qatar National Library

Gas Turbine Failure Classification using Acoustic Emissions with Wavelet Analysis and Deep Learning

Compared to vibration monitoring, acoustic emission (AE) monitoring in gas turbines is highly sensitive to changes that do not involve whole-body motion, such as wear, rubbing, and fluid-induced faults. AE signals captured by suitably mounted sensors can potentially provide early indications of abnormal turbine operation before such abnormalities manifest in structural vibration or emitted airborne noise. However, developing an online fault detection system requires extensive real-time data treatment to extract appropriate features and indicators from raw AE records. To build such a system for industrial turbines, researchers need to understand the AE-generating mechanisms associated with turbine operation and the sources of background noise. In this study, we aim to develop such an understanding using a small-scale turbine whose operational conditions can be modified safely to reflect both normal and faulty conditions. Our signal processing approach involves first extracting a time-series envelope using an averaging time selected to enhance major features and eliminate irrelevant noise. We then generate time-frequency features using a continuous wavelet transform, which are used to train a deep convolutional neural network to classify gas turbine conditions. The resulting model demonstrates high accuracy in classifying two normal running conditions and two faulty conditions at various turbine speeds. Overall, the proposed methodology offers a powerful tool for gas turbine condition monitoring, and we make all associated data available in open-source format to facilitate further research in this field. Other InformationPublished in: Expert Systems with ApplicationsLicense: http://creativecommons.org/licenses/by/4.0/See article on publisher's website: http://dx.doi.org/10.1016/j.eswa.2023.120684 </p