DARTS-CNN-BiLSTM: Intelligent Fault Diagnosis for Computer Numerical Control Machine Tool Feed System

As core equipment in high-end manufacturing, computer numerical control machine tools depend critically on the health of their feed systems, which directly affects machining quality and efficiency. To address fault diagnosis challenges under variable-speed and strong-noise conditions, this paper proposes a deep learning model named DARTS-CNN-BiLSTM. The key novelty lies in the first systematic integration of differentiable architecture search (DARTS) with a hybrid CNN-BiLSTM framework. DARTS automatically optimizes the convolutional neural network structure for spatial feature extraction, while the bidirectional long short-term memory (BiLSTM) captures bidirectional temporal dependencies. Global average pooling is used for feature reduction, and a softmax classifier enables end-to-end fault classification. This automated design eliminates the need for manual network tuning and feature engineering. Experimental results on two public datasets and a self-built dataset demonstrate that the proposed method outperforms advanced models such as Inception-BiLSTM and DenseNet. Specifically, our method maintains over 90% diagnostic accuracy under strong noise (signal-to-noise ratio $\ge$ -6 dB) and achieves 98.15% average accuracy on a variable-speed dataset. Ablation studies confirm the advantage of automated architecture design over manually tuned counterparts. These results validate the effectiveness and superiority of the proposed method for complex feed system fault diagnosis.