Noise-Robust Multiclass Classification of Diesel-Engine Fault States Based on a Dual-Decoder Denoising Autoencoder

To address noise interference and feature overlap in multiclass diesel-engine fault-state classification, this study proposes a dual-decoder denoising autoencoder with dual-channel feature fusion. Multi-SNR denoising training pairs are encoded by a shared encoder and reconstructed by a general denoising decoder under reconstruction and cross-SNR latent consistency constraints. With the encoder frozen, a normal-manifold decoder is trained on normal samples to generate a normal-state reference. The difference between the decoder outputs forms a normal-manifold residual that characterizes deviations from normal operation. Latent features are processed by a residual multilayer perceptron, whereas residuals are processed by multi-scale 1D convolution with efficient channel attention; gated fusion combines the two channels for classification. On the 3500-DEFault dataset, reconstruction MAE decreased by 93.75% at 0 dB and 90.86% at 15 dB. Anomaly detection based on normal-manifold residuals achieved an ROC-AUC of 0.9749. The proposed method attained an accuracy of 94.21% and a Macro-F1 of 94.28% on the independent test set, improving Macro-F1 by 3.07 percentage points over the DAE classifier. These results demonstrate the effectiveness of the proposed framework for noise-robust multiclass fault-state classification under the evaluated SNR conditions.