Aero-engine fault diagnosis based on time series anomaly detection

The fault diagnosis of aero-engines is confronted with a data skew issue，where the number of fault sam ples is significantly fewer than normal samples，and the fault samples can't adequately represent the entire operating conditions，resulting in poor generalization ability of conventional classification models. To overcome this issue，an improved deep support vector data description-based time series anomaly detection model is proposed. The long short-term memory（LSTM）network is employed to map the inputs and outputs of samples，forming temporal anomaly vectors with actual collected outputs. The deep support vector data description（SVDD）incorporating variational auto-encoder（VAE）is utilized to achieve anomaly detection for aero-engine time series data. The experimental verification is performed with a certain type of aero-engine ground test platform，and the model is com pared to with isolation forest（IF），transformer-based anomaly detection（TranAD）model，and GANomaly. The results show that the curve value calculated with the proposed model can reach to 0.987 8，has superior anomaly detection performance. The proposed model can effectively be applied to various anomaly detection and fault diagnosis tasks in aero-engine systems.