As the rolling bearing is the most important part of rotating machinery, its fault diagnosis has been a research hotspot. In order to diagnose the faults of rolling bearing under different noisy environments and different load domains, a new method named one-dimensional dilated convolution network with residual connection is proposed in this paper. The proposed method uses the one-dimensional time-domain signals of rolling bearing as input. Zigzag dilated convolution is introduced into convolution neural network, which can effectively improve the receptive field of the convolutional layer. A multi-level residual connection structure with different weight coefficients is constructed, so that the lower layer features of convolution neural network can be transferred to the upper layer, which improves the feature learning ability. Moreover, in order to enhance the useful features and weaken the useless features, we add the attention module Squeeze-and-Excitation (SE) block after each sub-residual structure. By using the rolling bearing datasets, the experimental results show that the proposed method can effectively diagnose faults of rolling bearing under different noisy environments and different load domains. Compared with other methods, the proposed method has higher accuracy.

Deep learning methods have been widely used in the field of fault diagnosis of rotating machinery. As one of the deep learning methods, multi-scale convolutional neural network (MSCNN) can achieve accurate fault diagnosis by extracting feature information of vibration signals at different scales. However, in practical applications, a small number of fault samples limit the performance of MSCNN. To overcome this problem, a small sample rotating machinery fault diagnosis method based on multi-branch and multi-scale dynamic convolutional network (MBSDCN) is proposed. Firstly, a feature splitting strategy is proposed, which splits a single input into multi-branch inputs to ensure that each multi-scale convolutional layer is matched to the appropriate input. Secondly, a channel reconstruction (CR) attention mechanism is designed, and CR attention mechanism can form a direct mapping of high-dimensional space with the fully connected layer through the reconstruction processing of channels, thus making the connection between feature channels closer. Finally, a novel multi-scale feature extraction model is constructed, in which multi-scale convolutional layers are applied to extract multi-branch input features, and the CR attention mechanism is used to calibrate the contribution of multi-scale convolution layers. The performance of MBSDCN is evaluated with CWRU rolling bearing dataset and UOC gearbox dataset and is compared with some advanced deep learning methods. The comparison results indicate that the proposed MBSDCN can accurately diagnose faults under small sample conditions. In addition, the experimental results in noisy environments indicate that the proposed MBSDCN has satisfactory anti-noise performance under small sample conditions and noisy environments.

Fault diagnosis of rotating machinery is difficult under the strong noisy environment. Although singular value decomposition (SVD) can remove noise from vibration signals, the singular value threshold is normally determined by expert experience. To solve this problem, a fault diagnosis method based on average descent rate (ADR)-SVD and two-dimensional residual neural network (Resnet) is proposed. First, ADR-SVD uses the ADR index to construct the singular value descent rate difference spectrum and uses the maximum value of the spectrum as the singular value threshold. The noise reduction process of ADR-SVD requires little expert experience. Then, in order to adaptively identify the fault features of the signals, we introduce Gramian angular difference field (GADF), which can transform the one-dimensional signals into two-dimensional images and preserve the temporal correlation of the one-dimensional signals. Finally, we construct a two-dimensional Resnet to learn image features and identify fault types. The proposed method is tested on Case Western Reserve University (CWRU) bearing dataset, Driveline Dynamic Simulator (DDS) gearbox dataset, and University of Connecticut (UoC) gearbox dataset under the strong noisy environment, which achieves the accuracies of 98.00%, 99.00%, and 98.88%, respectively. The accuracies of other deep learning methods and singular value difference spectrum method are below 95%. The comparisons show that the proposed method has better noise reduction effect and can diagnose the fault type more accurately.

Remaining useful life (RUL) prediction is crucial in the field of engineering, which can reduce the frequency of accidents and the maintenance cost of machinery. With the increasing complexity of rotating machinery, the data analysis methods based on deep learning have become the mainstream methods of prediction work. However, most of the current RUL prediction methods only use single-sensor data as input, which cannot effectively use multi-sensor data. In addition, as an advanced deep learning prediction method, temporal convolutional network (TCN) only uses the past time information of vibration data to determine the current health status of bearings, while ignoring the importance of future time information of vibration data. To solve the above problems, a bearing RUL prediction method based on multi-sensor data fusion and bidirectional-temporal attention convolutional network (Bi-TACN) is proposed in this paper. In multi-sensor data fusion, multi-sensor data are combined into multi-channel data, and a channel-weighted attention is designed to emphasize the importance of each sensor data. Compared with traditional multi-sensor data fusion, the proposed fusion method allows deep prediction networks to learn more useful feature information from multi-sensor data. Then, Bi-TACN is developed to predict the RUL of bearings. Bi-TACN is mainly composed of the forward TCN block and the backward TCN block, both of which can learn the past and future time information of multi-sensor data simultaneously. Moreover, a temporal attention mechanism is embedded in Bi-TACN to adaptively calibrate the weights of the two TCN blocks, so as to achieve dynamic feature fusion of past and future time information. RUL prediction experiments are carried out through Xi’an Jiao tong University bearing dataset and PHM 2012 bearing dataset respectively. Compared with the advanced prediction methods, the proposed method can accurately predict the RUL of more types of bearings and has low prediction errors.

In the field of data-driven fault diagnosis (FD), deep learning methods have proven their excellent performance, especially when dealing with complex signals from rotating equipment such as bearings. However, fault features in vibration signals are often mixed with noise features and distributed at different frequency scales, posing challenges for effective feature extraction. In order to solve this problem, this paper proposes a high frequency-multiscale cascade network (HF-MSCN), which enhances the noise suppression and feature learning capability of the model by combining a high-frequency convolutional block (HFCB) with a multi-scale cascade block (MSCB). HFCB effectively suppresses high-frequency noise through wide convolutional layers and self-attention mechanisms while still retaining essential high-frequency fault signals. MSCB enhances the interaction between convolutional layers at different scales by cascading the layers at different scales and strengthens the model’s ability to capture subtle fault features, especially when processing periodic fault pulse signals. Finally, we investigate the internal functioning of the network using time–frequency analysis methods in signal processing to improve the interpretability of deep learning methods in FD applications and further verify the enhanced effect of HFCB and MSCB on feature extraction. We validate the effectiveness of HF-MSCN on the case western reserve university dataset as well as a self-constructed bearing composite fault dataset, and the experimental results demonstrate that the network exceeds the performance of six state-of-the-art fault diagnostic methods in high-noise environments.