Parameters setting.

A key part of CNC machine tools is the rolling bearing, and thus, it is vital to employ a data-driven approach for fault diagnosis. This paper proposes a two-stage fusion sparse learning algorithm for fault data processing that can identify and diagnose the fault types of rolling bearings based on sensor measurement data. During the feature extraction phase, temporal features of sequential data within the big data are extracted using a Long Short - Term Memory (LSTM) network. Moreover, the classification learning stage contains a new sparse learning algorithm, which applies regularization on stochastic configuration networks (SCN). The iterative learning formula combines the alternating direction method of multipliers (ADMM) with the analysis of the quadratic equations theory. Simultaneously, the model’s inequality supervision mechanism is updated based on convergence analysis. This developed algorithm incorporates the benefits of LSTM in extracting temporal data characteristics, along with the sparsity, ease of convergence, and lightweight nature of SCN. Consequently, it mitigates the shortcomings of deep models in end-to-end applications, particularly in terms of interpretability and structural redundancy, thus making it suitable for deployment on edge devices. Finally, a fusion sparse learning model (LSTM--SCN) is introduced based on the two-stage learning algorithm for rolling bearing fault diagnosis. In the experiments on the benchmark dataset, the optimal sparsity degree of this algorithm for the Sparse Coding Network (SCN) reached 76.66%, which was 30% higher than that of the Pooling-based Sparse Coding Network (PSCN). Moreover, in the experiments based on the dataset of Case Western Reserve University (CWRU), the optimal test classification accuracy achieved was 97.51%, and the optimal sparsity degree for SCN reached 29.39%. These results verify that the proposed algorithm exhibits sparsity, demonstrates effectiveness, and is capable of identifying faults in rolling bearings.