It confronts great difficulty to apply the traditional rolling bearing fault diagnosis methods to adaptively extract features conducive to fault diagnosis under complex operating conditions, and obtaining numerous fault data under real operating conditions is difficult and costly. To address this problem, a fault diagnosis method based on two-dimensional time-frequency images and data augmentation is proposed. To begin with, the original one-dimensional time series signal is converted into two-dimensional time-frequency images by continuous wavelet transform to obtain the input data suitable for two-dimensional convolutional neural network (CNN). Secondly, data augmentation technique is employed to expand labeled fault data. Finally, the generated and original fault data are served as training samples to train the fault diagnosis model based on CNNs. Experimental studies are conducted on standard and real-world datasets to validate the proposed method and demonstrate its superiority over the traditional methods in detecting bearing faults.

As the key component of rotating machinery, effective and reliable fault diagnosis of rolling bearing is particularly critical for promoting production safety and economic benefits. The powerful representation learning ability of convolutional neural network (CNN) enables it to effectively extract fault information from vibration signals of rolling bearing. Nevertheless, challenges are faced by CNN in extracting features at multi-scale and capturing temporal features. With regard to this issue, a hybrid deep learning model that incorporates the multi-scale residual neural network (MSRN) with the enhanced gated recurrent unit (EGRU), namely MSRN-EGRU, is proposed in this paper. To begin with, MSRN is designed by introducing multi-scale structure and residual connections into CNN for extracting local features effectively and improving the feature representation of the model. Then, the extracted local features are input into EGRU to further extract temporal features, where EGRU is proposed by improving GRU structure and embedding scaled exponential liner unit (SELU), which enhances the nonlinear modeling and memory ability. Eventually, the obtained features are processed by α-Dropout and global average pooling before being inputted into the softmax layer for fault diagnosis. To validate the effectiveness of the proposed model, three baseline models and two ablation models were employed for comparative experiments with two bearing datasets. The experimental results reveal that the proposed model achieves commendable performance in terms of accuracy, robustness, and convergence for fault diagnosis of rolling bearing.

Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical equipment. With the rapid development of deep learning technology, the methods based on big data-driven provide a new perspective for the fault diagnosis of machinery. However, mechanical equipment operates in the normal condition most of the time, resulting in the collected data being imbalanced, which affects the performance of mechanical fault diagnosis. As a new approach for generating data, generative adversarial network (GAN) can effectively address the issues of limited data and imbalanced data in practical engineering applications. This paper provides a comprehensive review of GAN for mechanical fault diagnosis. Firstly, the development of GAN-based mechanical fault diagnosis, the basic theory of GAN and various GAN variants (GANs) are briefly introduced. Subsequently, GANs are summarized and categorized from the perspective of labels and models, and the corresponding applications are outlined. Lastly, the limitations of current research, future challenges, future trends and selecting the GAN in the practical application are discussed.

Most current rolling bearing fault diagnostic approaches presume that sufficient labeled data are available for training. However, labeled fault data collection in realistic engineering is time-consuming and expensive, whereas the majority of data available are unlabeled, restricting diagnostic performance. To alleviate the dependence of feature self-extraction model on rolling bearing fault data and improve the feature capture ability, a bearing fault diagnosis method based on feature-enhanced generative adversarial networks with an auxiliary classifier (AC-FEGAN) is proposed in this paper. Firstly, the non-saturating loss with gradient penalty term is introduced to circumvent the gradient vanishing and improve learning stability. Then, employing identity mapping of residual networks introduced into AC-FEGAN, representative faulty features can be automatically extracted, overcoming the restrictions of shallow convolutional neural networks in feature extraction. Simultaneously, to improve feature learning ability, the self-attention module embedded in the residual network accomplishes refined feature extraction by incorporating appropriate weight matrices into the faulty feature maps. Finally, the auxiliary classifier is employed as a pre-training model for the fault diagnosis model, with generated and unlabeled samples employed to fine-tune the auxiliary classifier to identify faulty bearings. The experimental results show that the proposed method can significantly improve the quality of generated samples while eliminating the reliance of existing diagnosis methods on data volume to achieve more accurate and effective fault diagnosis.

Rolling bearing fault diagnosis is a critical process for ensuring the safe and efficient operation of rotating machinery. With the rapid advancements in artificial intelligence technologies, data-driven approaches have offered new perspectives for rolling bearing fault diagnosis and have significantly advanced progress in this field. However, most existing review articles primarily focus on specific models or methods, such as individual deep learning architectures, or particular signal processing techniques, but they often lack a systematic summary of optimization strategies throughout the diagnostic process. As a result, researchers are often limited to particular models or techniques when studying fault diagnosis methods, making it difficult to gain a comprehensive understanding of the role of various optimization strategies. This limitation hinders both method selection and innovation. To address this issue, this paper systematically reviews the research progress related to measurement optimization strategies for rolling bearing fault diagnosis. It conducts a comprehensive analysis from two perspectives: data preprocessing and model algorithm optimization. First, this paper highlights the measurement optimization strategies in the data preprocessing stage, including data acquisition, signal denoising, data augmentation, and feature extraction. This provides reliable support for establishing a high-quality data foundation. Subsequently, the latest advancements in model algorithm optimization strategies are thoroughly summarized, encompassing both machine learning and deep learning. Detailed analyses are conducted on the critical roles of hyperparameter tuning, network structure design, and training strategy optimization in enhancing model performance. Additionally, the potential of emerging technologies such as transfer learning and model integration techniques is discussed, focusing on their capacity to improve model generalization and adaptability to complex operating conditions. Finally, thelimitations of current research are examined, and future development directions are proposed based on practical application requirements. These findings provide a reference for optimizing measurement strategies in rolling bearing fault diagnosis.