Currently, rolling bearing fault diagnosis faces dual challenges: significant variations in sample feature distributions caused by changes in operating conditions, and the scarcity of labeled fault samples. These factors severely undermine the generalization capability of traditional data-driven methods. To address these challenges, a multi-source domain feature fusion-domain adversarial neural network (MSDF-DANN) fault diagnosis model is proposed. Unlike conventional approaches that rely on unstable time-frequency representations, this study first employs variational mode decomposition and symmetric dot pattern to transform one-dimensional signals into two-dimensional snowflake maps, providing a geometrically consistent feature representation resilient to operating condition variations. Subsequently, a novel three-stage convolutional fusion module is designed to perform deep nonlinear integration of heterogeneous features from multiple source domains before adversarial adaptation. Furthermore, the mechanism of the adversarial weight is theoretically analyzed and experimentally verified to balance domain alignment and class discriminability. Finally, experiments on the Case Western Reserve University and Dynamic Diagnosis System datasets show that even with limited samples, MSDF-DANN has higher diagnostic accuracy and stability.

Reliable remaining useful life (RUL) prediction of rolling bearings serves as the core support for the prognostics and health management (PHM) of industrial rotating equipment. Although data-driven methods have achieved remarkable breakthroughs in prediction accuracy, the existing approaches still encounter three critical limitations: the insufficient informational dimensions of individual features, the poor adaptability between degraded features and prediction tasks in mainstream feature fusion models, and the channel-wise and temporal redundancy in multi-dimensional features that masks the inherent degradation trends of bearings. To tackle these challenges, this paper presents a novel rolling bearing RUL prediction method based on multivariate time series mixers with cross-channel convolutional fusion (CCF) (MTS-mixers-CCF). The proposed method initially constructs an initial feature set from bearing vibration signals. Subsequently, it establishes a feature-task adaptability evaluation framework using an adaptive differential evolution based on comprehensive evaluation metrics (CEM) algorithm to reconstruct a high-adaptability feature ensemble. Finally, it attains accurate RUL estimation through the MTS-mixers-CCF model, which reduces feature redundancy by means of factorization mechanisms and enhances inter-feature correlation via CCF layers. Experiments conducted on the widely adopted PHM2012 and XJTU-SY bearing datasets demonstrate that the MTS-mixers-CCF model outperforms traditional time series prediction methods and state of the art deep learning models, exhibiting significantly higher accuracy and stability for RUL prediction under ambiguous degradation trends. This research offers a robust and high-performance solution for rolling bearing RUL estimation, with promising application prospects in industrial PHM scenarios.

The stiffness model plays a crucial role in improving the performance of robots. During the operation of an underground mining cable-driven parallel robot (UMCDPR), insufficient stiffness can lead to motion instability, posing safety hazards. Additionally, the complexity of the underground mining environment, which is often accompanied by external disturbances, leads to offline stiffness indices failing when used underground as an optimal criterion. To address these problems, this article proposes a robust optimal stiffness direction (ROSD) index grounded in Rayleigh's theorem, which is characterized by three primary features: (1) strong robustness, (2) suitable for multi-trajectory optimization engineering problems, and (3) global visualization. Firstly, considering the influence of pulleys on the end-effector, the stiffness model of UMCDPR is modified. Secondly, a trajectory optimization method utilizing ROSD is introduced, incorporating the Kepler Conjecture and stiffness model correction. Finally, the characteristics of ROSD are validated through numerical simulations. Based on two numerical simulations, the ROSD index can serve as an optimal criterion for guiding stiffness optimization of UMCDPR. Furthermore, an optimal stiffness trajectory is obtained to meet the task objectives of UMCDPR.