Table 5 Classification performance of different approaches in fault detection.
Method | precision | recall | F1-score | Accuracy | Remark |
|---|---|---|---|---|---|
FCA | 0.988 | 0.992 | 0.989 | 0.989 | Best performance across all metrics |
FA | 0.967 | 0.967 | 0.966 | 0.966 | Comparable to PCA, but with better visualization |
PCA | 0.966 | 0.966 | 0.966 | 0.966 | Suitable for high-dimensional data |
RF [41] | 0.971 | 0.965 | 0.963 | 0.964 | Robust but lacks visualization capability |
ANN [42] | 0.969 | 0.956 | 0.959 | 0.955 | Requires complex hyper parameter tuning |
GB [43] | 0.932 | 0.932 | 0.931 | 0.931 | Prone to overfitting on small datasets |
DT [44] | 0.922 | 0.909 | 0.909 | 0.908 | Simple but sensitive to noise |