Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping

Liao, Zengbu; Zhan, Keyi; Zhao, Hang; Deng, Yuntao; Geng, Jia; Chen, Xuefeng; Song, Zhiping

doi:10.1016/j.ress.2024.110189

Reliability Engineering & System Safety

2024

DOI: 10.1016/j.ress.2024.110189

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Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping

Zengbu Liao,

Keyi Zhan,

Hang Zhao

et al.

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2024

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Cited by 2 publications

References 28 publications

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A domain generalization network for imbalanced machinery fault diagnosis

Guo,

Ju,

Zhang

2024

Sci Rep

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A domain generalization network for imbalanced machinery fault diagnosis

Guo,

Ju,

Zhang

2024

Sci Rep

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Intelligent Method of Identifying the Nonlinear Dynamic Model for Helicopter Turboshaft Engines

Vladov,

Banasik,

Sachenko

et al. 2024

Sensors

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This research focused on the helicopter turboshaft engine dynamic model, identifying task solving in unsteady and transient modes (engine starting and acceleration) based on sensor data. It is known that about 85% of helicopter turboshaft engines operate in steady-state modes, while only around 15% operate in unsteady and transient modes. Therefore, developing dynamic multi-mode models that account for engine behavior during these modes is a critical scientific and practical task. The dynamic model for starting and acceleration modes has been further developed using on-board parameters recorded by sensors (gas-generator rotor r.p.m., free turbine rotor speed, gas temperature in front of the compressor turbine, fuel consumption) to achieve a 99.88% accuracy in identifying the dynamics of these parameters. An improved Elman recurrent neural network with dynamic stack memory was introduced, enhancing the robustness and increasing the performance by 2.7 times compared to traditional Elman networks. A theorem was proposed and proven, demonstrating that the total execution time for N Push and Pop operations in the dynamic stack memory does not exceed a certain value O(N). The training algorithm for the Elman network was improved using time delay considerations and Butterworth filter preprocessing, reducing the loss function from 2.5 to 0.12% over 120 epochs. The gradient diagram showed a decrease over time, indicating the model’s approach to the minimum loss function, with optimal settings ensuring the stable training.

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Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping

Cited by 2 publications

References 28 publications

A domain generalization network for imbalanced machinery fault diagnosis

A domain generalization network for imbalanced machinery fault diagnosis

Intelligent Method of Identifying the Nonlinear Dynamic Model for Helicopter Turboshaft Engines

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