Particle erosion—a new concept of flow visualization and boundary layer investigations of rotating machines at high Reynolds numbers

Mann, Ben

doi:10.1016/s0043-1648(98)00319-6

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Gas turbine blades operate under such extreme conditions which could lead to blade damage, resulting in catastrophic failures. Within the timescale of commissioning to overhaul, several types of blade faults can develop, including blade erosion (Mann, 1998; Metwally, 1995), fatigue (Bhaumik, 2006; Hou et al, 2002a; Tong et al, 2020), creep (Bhaumik et al, 2002; Mazur, 2005), and flutter (Meher-Homji and Gabriles, 1998; Mehdi et al, 2008). Other critical failures could also occur such as compressor fouling (Cyrus et al, 2004; Kurz, 2001; Lu et al, 2016), seal rubbing (Bentley, 1974; Yu et al, 2002), and bearing damage (Caesarendra et al, 2016; Hou et al, 2022b; Patil et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Fault classification using convolutional neural networks and color channels for time-frequency analysis of acoustic emissions

Nashed

Renno

Mohamed

2023

Journal of Vibration and Control

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We present a novel method for real-time fault classification using the time history of acoustic emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty conditions across multiple turbine speeds. Time-frequency features are extracted using the continuous wavelet transform, and for each signal, the root mean square (RMS) and kurtosis are calculated. We employ a color mapping technique to combine the time-frequency and statistical features into a single red–green–blue (RGB) image. The red channel is mapped to the time-frequency data, whereas the green and blue channels are mapped to the RMS and kurtosis, respectively. Subsequently, a deep convolutional neural network is trained on the generated images to classify the gas turbine condition. We show that the proposed model can form an online monitoring system using AEs to classify multiple running conditions at various turbine speeds. The methodology not only achieves real-time classification of faults but also minimizes the human intervention in identifying these faults. The datasets and codes used in this paper will be openly available.

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