Table 1 Fault prediction research of aircraft engine in recent years.
From: Fault prediction of aircraft engine based on adaptive hybrid sampling and BiLSTM
Publications | Problems | Methods |
|---|---|---|
Kobayashi and Simon15 | Actuator fault prediction and isolation | Kalman filters |
Yan16 | Real-world aircraft engine fault diagnostic | Random forest |
Zhang et al.17 | Fault prediction and isolation (FDI) method for aircraft engines | Nonlinear adaptive estimation techniques |
Tamilselvan and Wang18 | Aircraft engine fault diagnostic | Deep belief learning based health state classification |
Babu et al.19 | Remaining useful life prediction of aircraft engine | Deep convolutional neural network (CNN) |
Wang et al.20 | Fault prediction of civil aircraft engine | Adaptive estimation of instantaneous angular speed (IAS) |
Xi et al.21 | Fault prediction of aircraft engine | Least squares support vector machine (LSSVM) |
Zhao et al.22 | Fault prediction of aircraft engine | Imbalanced kernel extreme learning machine (KELM) |
Zhao et al.23 | Fault prediction of aircraft engine | Dynamic radius support vector data description (DR-SVDD) |
