Fault diagnosis in rolling bearings is an indispensable part of maintaining the normal operation of modern machinery, especially under the varying operating conditions. In this paper, an end-to-end adaptive anti-noise neural network framework (AAnNet) is proposed to solve the bearing fault diagnosis problem under heavy noise and varying load conditions, which takes the raw signal as input without requiring manual feature selection or denoising procedures. The proposed AAnNet employs the random sampling strategy and enhanced convolutional neural networks with the exponential linear unit as the activation function to increase the adaptability of the neural network. Moreover, the gated recurrent neural networks with attention mechanism improvement are further adopted to learn and classify the features processed by the convolutional neural networks part. Besides, we try to explain how the network works by visualizing the intrinsic features of the proposed framework. And we explore the effect of the attention mechanism in the proposed framework. Experiments show that the proposed framework achieves state-of-the-art results on two datasets under varying operating conditions.

Most of the recent image segmentation methods have tried to achieve the utmost segmentation results using large-scale pixel-level annotated data sets. However, obtaining these pixel-level annotated training data is usually tedious and expensive. In this work, we address the task of semisupervised semantic segmentation, which reduces the need for large numbers of pixel-level annotated images. We propose a method for semisupervised semantic segmentation by improving the confidence of the predicted class probability map via two parts. First, we build an adversarial framework that regards the segmentation network as the generator and uses a fully convolutional network as the discriminator. The adversarial learning makes the prediction class probability closer to 1. Second, the information entropy of the predicted class probability map is computed to represent the unpredictability of the segmentation prediction. Then, we infer the label-error map of the segmentation prediction and minimize the uncertainty on misclassified regions for unlabeled images. In contrast to existing semisupervised and weakly supervised semantic segmentation methods, the proposed method results in more confident predictions by focusing on the misclassified regions, especially the boundary regions. Our experimental results on the PASCAL VOC 2012 and PASCAL-CONTEXT data sets show that the proposed method achieves competitive segmentation performance.

Recently, deep neural networks have achieved great success in bearing fault diagnosis. Most existing methods are developed under the assumption that the bearing vibration signals are collected under the same machine operating conditions. However, bearing fault diagnosis under cross-domain conditions will suffer from domain shift problems if the neural network is only trained with the source domain data. Moreover, acquiring enough labeled data from the target domain will be expensive and time-consuming. To address the above problems, this paper proposes an end-to-end multi-adversarial cross-domain neural network for bearing fault diagnosis, which takes labeled source domain data and unlabeled target domain data to achieve the cross-domain bearing fault diagnosis under cross-load conditions and cross-machine conditions. The proposed method employs multi-adversarial training to automatically extract the domain-invariant features from source and target domains instead of manually designing features, which combines domain-adversarial learning and mini-max entropy adversarial learning to adversarially reduce the domain discrepancy between the source and target domains and alleviate the class misalignment problem. The results of the cross-load and the cross-machine experiments prove the effectiveness of the proposed method, and the proposed method provides a promising tool for cross-domain bearing fault diagnosis.

In recent years, deep learning has been gradually applied in bearing fault diagnosis thanks to its powerful learning and representation ability. However, deep learning-based methods are usually computationally intensive and have redundant parameters. In this paper, we propose an end-to-end structure optimized deep convolutional neural network for bearing fault diagnosis using the local sparse structure. The width and depth of the network are increased and the redundant parameters are reduced while maintaining the representation capabilities and performance of the network. The local sparse structure increases the computational efficiency and reduces the risk of overfitting. The proposed method directly employed the raw signals as input without extra feature extraction with the ability that could distinguish both the bearing fault types and the corresponding severity. Experiments show that the proposed method achieves similar performance compared to the original method by using 46.47% parameters as used in the original study, even under the noisy environment.