Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault detection, classification, and identification tasks. Based on these observations, this article proposes a novel integrated multitasking intelligent bearing fault diagnosis scheme with the aid of representation learning under imbalanced sample condition, which realizes bearing fault detection, classification, and unknown fault identification. Specifically, in the unsupervised condition, a bearing fault detection approach based on modified denoising autoencoder (DAE) with self-attention mechanism for bottleneck layer (MDAE-SAMB) is proposed in the integrated scheme, which only uses the healthy data for training. The self-attention mechanism is introduced into the neurons in the bottleneck layer, which can assign different weights to the neurons in the bottleneck layer. Moreover, the transfer learning based on representation learning is proposed for few-shot fault classification. Only a few fault samples are used for offline training, and high-accuracy online bearing fault classification is achieved. Finally, according to the known fault data, the unknown bearing faults can be effectively identified. A bearing dataset generated by rotor dynamics experiment rig (RDER) and a public bearing dataset demonstrates the applicability of the proposed integrated fault diagnosis scheme.

Unsupervised domain adaptation is widely used for fault diagnosis under variable working conditions. However, loss oscillation and slow convergence, which are caused by the dynamically varying alignment of targets during domain adaptation, are ignored. Therefore, in this article, a Gaussian mixture variational-based transformer domain adaptation (GMVTDA) fault diagnosis method is proposed. A feature extractor based on transformer layers is designed to capture long-term dependence information and local features. Subsequently, a domain alignment term is proposed to project the features learned from both working conditions into the common assistance distribution and make them follow the same distribution after the alignment process. In addition, considering that fault diagnosis is a multiclassification process, a Gaussian mixture is utilized to build the common assistance distribution. Ultimately, the proposed GMVTDA is applied to bearing fault diagnosis under variable working conditions, and the experimental results prove its effectiveness.

The application of deep learning-based rolling bearing fault diagnosis methods in high reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep learning models, in order to improve the performance of rolling bearing fault diagnosis (RBFD), has led to difficulties in its application in low-resource scenarios. Based on these facts, a new neural network, restricted sparse networks (RSNs), is proposed in this work. Firstly, a restricted sparse frequency domain space (RSFDS) is proposed for the interpretable representation of rolling bearing fault features (RBFFs) based on the quadratic complex domain equation. Secondly, an interpretable multi-channel fusion mechanism is designed to map RBFFs to RSFDS. Furthermore, a high power feature extraction module is developed to extract RBFFs in an efficient and easy-to-understand manner. Finally, an end-to-end RBFD network is provided for high reliability and resource-constrained scenarios. The experimental results show that RSNs have favorable fault diagnosis accuracy performance that is parallel to the state-of-the-art methods. More importantly, the model size of the proposed network only accounts for 20%-30% of the conventional methods.

Lack of massive-labeled samples may cause the performance degradation of intelligent bearing fault diagnosis methods under variable working conditions. Unsupervised domain adaptation (UDA)-based methods can effectively alleviate this problem by decreasing the distribution discrepancy between two dataset. However, most current UDA-based methods focus only on minimizing the marginal distribution. The influence of misclassification of samples near the classification boundaries is ignored. This may lead to the result that even though the marginal distributions are aligned well, the diagnosis accuracy is not ideal enough. On account of this, an unsupervised contrastive domain adaptation network (UCDAN) for bearing fault diagnosis under variable working conditions is proposed. A deep convolution feature extractor is constructed to extract high-level features. At the same time, a domain-adversarial total loss function based on the strategy of minimizing the domain distribution discrepancy is designed to obtain domain-shared discriminant features for aligning the marginal distribution. Moreover, a contrastive estimation term is designed to quantize the similarity of data distribution and maximize the consistency between the samples from the same health conditions. Thereby reducing the probability of samples being misclassified near or on the boundary and improving diagnosis performance. And information entropy is used to avoid most samples are assigned to the same cluster. The effectiveness of UCDAN is confirmed by examining various fault diagnosis situations with domain discrepancies across the source and target conditions, using experimental data from two bearing systems.

A certainty and transferability guided few-shot domain adaptation network is proposed to address few-shot open-set cross-domain fault diagnosis in this article. The proposed method is composed of a feature extractor, a certainty-guided prototypical contrastive module and a transferability weighting domain adaptation module. The certainty-guided prototypical contrastive module based on samples informative importance is designed to enhance the data sensitivity with limited samples while achieving well class separation for open-set scenarios. The module infers informative importance of samples to guide method learn more effective representations. Meanwhile, correlation and uniformity principles are incorporated to alleviate prototype collapse. The transferability weighting domain adaptation module is designed to address great domain gaps and negative transfer caused by asymmetrical label spaces. The module quantifies sample transferability and down-weights the irrelevant samples based on their transferability scores. Experimental results on few-shot open-set cross-domain bearing fault diagnosis tasks demonstrated the superior and effectiveness of the proposed method.

Unsupervised domain adaptation methods have found broad application in fault diagnosis under variable working conditions. However, the performance of these models significantly deteriorates when the samples in the source and target working conditions are limited. To tackle this issue of few-shot fault diagnosis under variable working conditions, a prototypical contrastive based domain adaptation method (PCDA) is proposed. The proposed method contains three key modules: a feature extractor, a prototypical contrastive based few-shot classifier and a domain adaptation module. Initially, the feature extractor is used to extract features from limited samples. Subsequently, the prototypical contrastive based few-shot classifier is used to identify the fault categories. Alongside, a corresponding prototypical contrastive loss function is designed to dynamically estimate the concentration of feature distribution around each prototypeand encourage the representations learned from the same category to be closer. Simultaneously, the method is guided to generate meaningful representations in a self-supervised setting, thereby enhancing the representation ability of the model. Furthermore, the domain adaptation module is utilized to bridge the great gap between two working conditions. Ultimately, PCDA is applied to two cross-domain few-shot bearing fault diagnosis tasks. These tasks include working conditions transfer tasks and machine types transfer tasks to verify the effectiveness of the proposed method.