The rotating machinery system consists of several key components such as bearings and gears. The operating condition of the bearings directly affects equipment safety and production efficiency. However, traditional bearing fault diagnosis methods face challenges in complex operating conditions, including insufficient local feature extraction, severe noise interference, and difficulty in integrating global information due to the heterogeneity of multi-sensor data. To address these issues, this paper proposes a multi-sensor and multi-task fault diagnosis method based on the multi-scale hidden state interaction network (MHSNet). In terms of feature extraction, MHSNet integrates deep separable convolutions with hidden state-space models. By introducing multi-scale convolution units, it captures local details under different receptive fields. Additionally, the selective hidden state modeling mechanism of the Mamba module overcomes the limitations of conventional convolution networks’ local receptive fields, enabling the modeling of periodic impulses and long-range dependencies in signals. In the data fusion layer, a dynamic state space fusion module is designed to achieve parameterized interaction and adaptive alignment of multi-sensor data within the hidden state space, effectively alleviating the distribution differences and redundancy issues between multi-source information. Through the collaborative extraction of complementary features between tasks, the model further enhances robustness and discriminative accuracy under conditions of data imbalance and noise interference. Extensive experiments conducted on real bearing data and multi-condition testing platforms demonstrate that MHSNet consistently achieves high diagnostic accuracy and condition classification performance. It outperforms traditional single-modal and heterogeneous multi-sensor signal-based diagnostic networks, highlighting its significant advantages in multi-sensor collaborative representation, global and local feature fusion, and noise suppression.

Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks.