To satisfy the requirements of the end-to-end fault diagnosis of rolling bearings, a hybrid model, based on optimal SWD and 1D-CNN, with the layer of multi-sensor data fusion, is proposed in this paper. Firstly, the BAS optimal algorithm is adopted to obtain the optimal parameters of SWD. After that, the raw signals from different channels of sensors are segmented and preprocessed by the optimal SWD, whose name is BAS-SWD. By which, the sensitive OCs with higher values of spectrum kurtosis are extracted from the raw signals. Subsequently, the improved 1D-CNN model based on VGG-16 is constructed, and the decomposed signals from different channels are fed into the independent convolutional blocks in the model; then, the features extracted from the input signals are fused in the fusion layer. Finally, the fused features are processed by the fully connected layers, and the probability of classification is calculated by the cross-entropy loss function. The result of comparative experiments, based on different datasets, indicates that the proposed model is accurate, effective, and has a good generalization ability.

Based on the non-stationary and non-linear acceleration signals, a rapid data driven method for fault diagnosis in gear transmission systems, which based on swarm decomposition (SWD) algorithm, improved multi-scale reverse dispersion entropy (improved MRDE) algorithm, and bidirectional long short-term memory network (Bi-LSTM), is proposed. Firstly, every segment in the original signals is decomposed into several OCs with simple fault information by SWD algorithm. Secondly, the proposed improved MRDE algorithm is adopted to further extract the features of the original signal and the decomposed signals under different scale factors, and the features are combined into a bigger feature vector next. Finally, the datasets composed by feature vectors are divided into train and test datasets to train and validate Bi-LSTM network, so as to recognize and classify different fault signals intelligently. The proposed method of fault diagnosis in this paper is verified by the signals under different types of faults are collected from the Wind Turbine Drivetrain Diagnostics Simulator (WTDDS). And the results of the experiment show that, it can recognize and classify the types of gear transmission system’ fault diagnosis quickly and accurately, and has its advantages in stability, determination and efficiency.

A decision-level multimodal fusion deep learning strategy is proposed for the effective fault detection of rolling bearings based on long-term fault signals collected from multiple sensors. First, key features are extracted from the multimodal signal set using singular spectrum analysis (SSA), and these features are transformed into a composite dataset that combines short-time Fourier transform (STFT) images and time series data. Based on this, a recursive gated convolutional neural network (RGCNN) is designed to process the STFT image data, while a 1D convolutional neural network (1DCNN) is specifically optimized for training with time series data. Furthermore, decision-level multimodal feature fusion is achieved by applying a weighted average method to integrate the features from different deep learning models, aiming to obtain more comprehensive fault prediction results. The proposed method, multimodal fusion fault detection (MFFD), is validated on the Paderborn and Ottawa rolling bearing datasets, which include various typical faults. Experimental results demonstrate the effectiveness of the proposed approach. Compared to traditional single-modality deep learning models, the proposed method shows significant improvements in fault diagnosis accuracy and generalization capability.

The vibration signals of faulty bearings contain rich feature information in both the time and frequency domains. Effectively leveraging this information is crucial, especially when addressing imbalanced bearing fault datasets, as it can significantly enhance the performance of fault diagnosis models. However, existing GAN models and diagnostic methods do not fully exploit these domain-specific features. To overcome this limitation, a novel fault diagnosis method is proposed, based on the Adaptive Wavelet-Like Transform Generative Adversarial Network (AWLT-GAN) and ensemble learning. In the first stage, AWLT-GAN is used to balance the bearing fault dataset by integrating time- and frequency-domain feature information. AWLT-GAN embeds an adaptive wavelet-like transform neural network into the generator as an adaptive layer and employs a dual-discriminator architecture. This design allows the network to simultaneously learn fault characteristics from both domains within a single training session, enhancing the quality of the synthetic fault data. Next, an ensemble learning approach is applied, combining time- and frequency-domain models, with the final classification determined through a soft voting mechanism. Experimental results demonstrate that the vibration signals generated by AWLT-GAN effectively replicate the feature distribution of real data, confirming its high performance. The fault diagnosis model, developed using these high-quality synthetic samples, accurately captures fault characteristics embedded in both the time and frequency domains, resulting in enhanced diagnostic performance. The proposed approach not only addresses the imbalance in bearing fault datasets but also significantly improves diagnostic accuracy.

To tackle the issue of the traditional intelligent diagnostic algorithm’s insufficient utilization of correlation characteristics within the time series of fault signals and to meet the challenges of accuracy and computational complexity in rotating machinery fault diagnosis, a novel approach based on a recurrence binary plot (RBP) and a lightweight, deep, separable, dilated convolutional neural network (DSD-CNN) is proposed. Firstly, a recursive encoding method is used to convert the fault vibration signals of rotating machinery into two-dimensional texture images, extracting feature information from the internal structure of the fault signals as the input for the model. Subsequently, leveraging the excellent feature extraction capabilities of a lightweight convolutional neural network embedded with attention modules, the fault diagnosis of rotating machinery is carried out. The experimental results using different datasets demonstrate that the proposed model achieves excellent diagnostic accuracy and computational efficiency. Additionally, compared with other representative fault diagnosis methods, this model shows better anti-noise performance under different noise test data, and it provides a reliable and efficient reference solution for rotating machinery fault-classification tasks.

To enhance the social navigation performance of mobile robots in crowded environments, we propose a novel framework—Prediction and Fuzzy Timed Elastic Band (PF-TEB) for robot social navigation. Our framework incorporates predicted pedestrian trajectories into pedestrian proxemics modeling as a social layer cost map, allowing the robot to better interpret pedestrian movement intentions and proactively avoid potential conflicts. Additionally, social constraints are integrated into TEB to ensure that both pedestrian comfort and safety are prioritized during trajectory planning. To address the adaptability limitations of conventional methods in highly dynamic, crowded environments, we introduce three type-2 fuzzy logic controllers. These controllers dynamically adjust the optimization weights of the corresponding constraints in the objective function based on real time environmental data, enabling timely modifications to the planned trajectory and significantly enhancing the flexibility and adaptability of the navigation system. The simulation experiments demonstrate that our method performs well in social metrics by respecting pedestrian comfort and its effectiveness has been validated on the Tracer robot in real-world environments.