Attention Mechanism in Intelligent Fault Diagnosis of Machinery: A Review of Technique and Application

Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review

Rolling bearing fault diagnosis with limited imbalance data is significant and challenging. It is​ a nice attempt to generate data for balancing datasets. In this paper, a wavelet capsule generative adversarial network (WCGAN) is proposed to address this issue. Firstly, the Harr wavelet is introduced into GAN to construct wavelet transform GAN (WTGAN). It keeps convolutional neural networks (CNNs) shift-invariant to extract the deep features of the data. Secondly, WCGAN is developed to alleviate CNNs’ incomplete analysis of signal information, which replaces part of CNNs in WTGAN with capsule networks. Thirdly, a novel loss function is designed for WCGAN to maintain a smooth training process and improve the quality of the generated data. Furthermore, various experiments are conducted in multiple ways to confirm the effectiveness and accuracy of the novel method. Results indicate that the proposed method balances the dataset and surpasses other advanced approaches in imbalanced data diagnosis with potential. 

Data-augmented wavelet capsule generative adversarial network for rolling bearing fault diagnosis

As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Deep learning-based fault diagnosis models constructed from imbalanced datasets would meet severe performance degradation when the number of samples for fault classes is much smaller than the normal category. Recent feature-learning-based methods have shown that learning the discriminative feature representation helps construct the well-performing fault classifier in imbalanced fault diagnosis. However, these methods have limited discriminative feature extraction abilities when applying them to a more practical but not well-studied class-imbalance scenario, where only the normal condition has a large amount of data while sample sizes of all fault classes are small. To address this issue, a novel feature-learning-based method called Class-aware Supervised Contrastive Learning (CA-SupCon) is proposed. Supervised contrastive learning (SupCon) is adopted for the first time in imbalanced fault diagnosis to optimize the feature difference between any two classes by leveraging category information. Additionally, a class-aware sampler (CA) is designed to rebalance data distribution within each mini-batch during training, which improves the ability of SupCon to enlarge the feature distance between any two minority fault conditions. By effectively integrating SupCon and CA, the proposed CA-SupCon framework can obtain a more discriminative feature space with better intra-class compactness and inter-class separability, and achieves good performance under the above class-imbalance scenario. Extensive experiments on two open-source datasets demonstrate the effectiveness of the proposed method. Code is available at https://github.com/JiyangZhang-UESTC/CA-SupCon . 

jun li

Papers

Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method