Miao Dongyan, Feng Kun, Xiao Yuan, Li Zhouzheng, Gao Jinji
State Key Laboratory of High-End Compressor and System Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Sensors (Basel). 2024 Jan 31;24(3):941. doi: 10.3390/s24030941.
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.
燃气轮机振动数据在时变条件下可能会呈现出相当大的差异，这给神经网络异常检测带来了挑战。我们首先提出了一个用于时变运行条件下燃气轮机振动频谱处理的框架，以辅助神经网络捕捉微弱信息的能力。该框架包括对频谱进行缩放，以使所有与转速相关的频率分量对齐，并以自适应方式对频率幅度进行归一化。采用递减β变分自编码器来学习频谱特征和进行异常检测，同时提出了一个多类别异常指标以适应各种运行条件。最后，使用一个在时变运行条件下发生叶片外物损伤（FOD）故障的数据集来验证该框架和异常检测。结果表明，所提出的方法能够有效减少时变条件下的频谱差异，并且还能在运行期间检测到FOD故障，而使用传统方法很难识别这些故障。
