Fault diagnosis in industrial process is essential for ensuring production safety and efficiency. However

existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains

resulting in suboptimal performance and robustness. Therefore

this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive (SmdaNet). First

the method uses deep belief networks (DBN) to build a diagnostic model. Hard samples are mined based on the loss values

dividing the data set into hard and easy samples. Second

elastic weight consolidation (EWC) is used to train the model on hard samples

effectively preventing information forgetting. Finally

the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions. Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy

robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.