A Clustering Fault Diagnosis Method for Gas Turbine Performance Parameters Based on t-SNE

Miao, Dongyan; Liu, Baoxia; Li, Zhouzheng; Li, Gang; Zhang, Peng; Feng, Kun

doi:10.1109/phm-yantai55411.2022.9941826

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…Gas turbines are highly expensive industrial equipment that find widespread use in numerous fields such as ship propulsion, gas transmission, and electric power generation due to their reliability as well as practicality [1,2]. Functioning within challenging environments marked by elevated temperatures, increased pressures, and high rotational speeds, components of gas turbines are susceptible to failures.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Anomaly Detection under Time-Varying Operation Conditions Based on Spectra Alignment and Self-Adaptive Normalization

Miao,

Feng,

Xiao

et al. 2024

Sensors

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Gas turbine vibration data may exhibit considerable differences under time-varying conditions, which poses challenges for neural network anomaly detection. We first propose a framework for a gas turbine vibration frequency spectra process under time-varying operation conditions, assisting neural networks’ ability to capture weak information. The framework involves scaling spectra for aligning all frequency components related to rotational speed and normalizing frequency amplitude in a self-adaptive way. Degressive beta variational autoencoder is employed for learning spectra characteristics and anomaly detection, while a multi-category anomaly index is proposed to accommodate various operating conditions. Finally, a dataset of blade Foreign Object Damage (FOD) fault occurring under time-varying operating conditions was used to validate the framework and anomaly detection. The results demonstrate that the proposed method can effectively reduce the spectra differences under time-varying conditions, and also detect FOD fault during operation, which are challenging to identify using conventional methods.

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Section: Introductionmentioning

confidence: 99%