Rolling bearings are critical components of industrial equipment, and predicting their remaining useful life (RUL) is a challenging task. This article proposes a new end-to-end model (MDSCT) to achieve efficient and accurate prediction of bearing RUL. MDSCT uses the raw vibration signals collected by sensors for prediction, without relying on a large amount of prior knowledge. The feature extraction backbone of this model is composed of an MDSC attention module that integrates multiple parallel depth-wise separable convolutions and an efficient attention mechanism, combined with a tansformer encoder (PPSformer) optimized using patch embedding and probesparse self attention techniques. They are respectively used to capture local subtle features and global dependent features in degraded signals. This article also proposes an improved adaptive activation function AdaptH_Swish to enhance the model's ability to model nonlinear relationships. To comprehensively verify the comprehensive performance of the model, this paper conducted detailed ablation and comparative experiments using two standard datasets, PHM2012 and XJTU-SY. The experimental results not only confirm the rationality and efficiency of the model structure design, but also demonstrate significant advantages in three evaluation indicators compared to various existing methods, fully demonstrating the high generalization and strong robustness of the MDSCT model in bearing RUL prediction tasks.

Fire incidents pose a significant threat to human life and property security. Accurate fire detection plays a crucial role in promptly responding to fire outbreaks and ensuring the smooth execution of subsequent firefighting efforts. Fixed-size convolutions struggle to capture the irregular variations in smoke and flames that occur during fire incidents. In this paper, we introduce FireViT, an adaptive lightweight backbone network that combines a convolutional neural network (CNN) and transformer for fire detection. The FireViT we propose is an improved backbone network based on MobileViT. We name the lightweight module that combines deformable convolution with a transformer as th DeformViT block and compare multiple builds of this module. We introduce deformable convolution in order to better adapt to the irregularly varying smoke and flame in fire scenarios. In addition, we introduce an improved adaptive GELU activation function, AdaptGELU, to further enhance the performance of the network model. FireViT is compared with mainstream lightweight backbone networks in fire detection experiments on our self-made labeled fire natural light dataset and fire infrared dataset, and the experimental results show the advantages of FireViT as a backbone network for fire detection. On the fire natural light dataset, FireViT outperforms the PP-LCNet lightweight network backbone for fire target detection, with a 1.85% increase in mean Average Precision (mAP) and a 0.9 M reduction in the number of parameters. Additionally, compared to the lightweight network backbone MobileViT-XS, which similarly combines a CNN and transformer, FireViT achieves a 1.2% higher mAP while reducing the Giga-Floating Point Operations (GFLOPs) by 1.3. FireViT additionally demonstrates strong detection performance on the fire infrared dataset.

Although deep learning methods based on single sensors are widely applied in fault diagnosis, leveraging multi-sensor data to learn useful information remains a challenge. To fully utilize multi-sensor information, this paper proposes a lightweight improvement of the EfficientNetV2 architecture, combined with sensor fusion technology and transfer learning techniques, to develop an efficient and reliable new method specifically for motor fault diagnosis. First, the continuouswavelet transform is utilized to convert the signals from various sensors into time-frequency images, and the Mallat algorithm is employed to decompose each image into sub-band coefficients at different levels. Secondly, a fusion reconstruction method is constructed using coefficient absolute maximum and weighted average fusion rules to integrate the sub-band coefficients of multi-sensor time-frequency images at different levels. Subsequently, EfficientNetV2 is improved to enhance the model’s feature extraction capabilities, computational efficiency, and achieve lightweight effects. The EfficientNetV2-M0 network modifies the model’s depth and width multiplicity factors, reducing parameters and computational complexity. Furthermore, this network incorporates Diverse Branch Block (DBB) and Multidimensional Collaborative Attention (MCA) to enhance feature extraction under complex backgrounds, and the maximum cross-entropy loss function is improved by using label smoothing and focal loss to dynamically adjust the classification weights for improved accuracy. The network leverages pre-trained models obtained through transfer learning techniques for deployment, combining multi-sensor information fusion and the improved lightweight model for fault diagnosis applications. Finally, a fault diagnosis experiment is conducted using a motor state dataset. The experimental results demonstrate that the proposed method outperforms the control method in terms of diagnostic performance and robustness, and it exhibits excellent performance even under conditions of small sample data.

The fault symptoms of rolling bearings are subject to various interferences in complex industrial environments, so achieving accurate, robust, and generalized fault diagnosis has become a key research direction. This article proposes a rolling bearing fault diagnosis method based on 1D-Inception-SE, which combines the 1D-Inception network model with Squeeze and Excitation Attention and can directly use the original vibration signals for fault diagnosis. The method incorporates the Adaptive Batch Normalization algorithm to enhance the model’s generalization performance in the presence of noise interference and cross-load diagnostics. Performance tests on Paderborn University Bearing and Case Western Reserve University datasets show that our approach achieves superior recognition accuracy compared to other models under similar and varied loads, as well as different signal to noise ratio. Ablation and visualization tests confirm the rationality and effectiveness of the model structure.