Search

Bearings, as the key mechanical components of rotary machinery, are widely used in modern aerospace equipment, such as helicopters and aero-engines. Intelligent fault diagnosis, as the main function of prognostic health management systems, plays a critical role in maintaining equipment safety in aerospace applications. Recently, data-driven intelligent diagnosis approaches have achieved great success due to the availability of large-scale, high-quality, and complete labeled data. However, in a real application, labeled data is often scarce because it requires manual labeling, which is time-consuming and labor-intensive. Meanwhile, health monitoring data are usually scattered in different regions or equipment in the form of data islands. Traditional fault diagnosis techniques fail to gather enough data for model training due to data security, economic conflict, relative laws, and other reasons. Therefore, it is a challenge to effectively combine the data advantages of different equipment to develop an intelligent diagnosis model with better performance. To address this issue, a novel clustering federated learning (CFL) method with a self-attention mechanism is proposed for bearing fault diagnosis. Firstly, a deep neural network with a self-attention mechanism is developed in a convolutional pipe for feature extraction, which can capture local and global information from raw input. Then, the CFL is further constructed to gather the data from different equipment with similar data distribution in an unsupervised manner. Finally, the CFL-based diagnosis model can be well trained by fully utilizing the distributed data, while ensuring data privacy safety. Experiments are carried out with three different bearing datasets in aerospace applications. The effectiveness and the superiority of the proposed method have been validated compared with other popular fault diagnosis schemes. [ABSTRACT FROM AUTHOR]

In recent years, deep learning methods, especially convolutional neural network (CNN), have received extensive attentions and applications in fault diagnosis. However, recent studies have shown that the shift-invariance of CNN is not good enough, resulting in fragile feature extraction and sharp reduction in model performance when the shift occurs in the input. To improve the shift-invariance of CNN, considering the periodic characteristics of vibration signals, a multiscale CNN with feature alignment (MSCNN-FA) is proposed for bearing fault diagnosis under different working conditions. First, by analyzing the operating principles of the convolutional layer and pooling layer, a feature alignment module including single-stride convolution layer, adaptive max-pooling layer, and global average pooling layer is designed to obtain aligned features. Next, to extract shift-invariant robust features from vibration signals, a multiscale convolution strategy is utilized, and a feature-aligned multiscale feature extractor is constructed. Finally, a classifier composed of fully connected (FC) layers is constructed for bearing fault diagnosis. The effectiveness of the method is verified by a rolling bearing experiment, which outperforms other related existing CNN-based methods in terms of diagnosis accuracy and feature robustness. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has become a promising tool for rotary machinery fault diagnosis, but it works well only when testing samples and training samples are independent and identically distributed. In practice, rotary machinery usually works under variable speed. The change of speed leads to the variation of samples’ distribution, which can significantly decrease the performance of the deep learning model. Scholars try to utilize transfer learning techniques for solving this problem. However, most exiting methods can just work well under target speed instead of all speed, while the target samples are always required in model training. In this article, a deep semisupervised domain generalization network (DSDGN) is proposed for rotary machinery fault diagnosis under variable speed, which can generalize the model to the fault diagnosis task under unseen speed. Under the setting of semisupervised domain generalization, only one fully labeled source (LS) domain data set and one totally unlabeled source (US) domain data set are available during training. To make full use of these data, the proposed method simultaneously utilizes Wasserstein generative adversarial network with gradient penalty (WGAN-GP)-based adversarial learning and pseudolabel-based semisupervised learning for training. The transmission and bearing fault diagnosis cases are utilized for evaluation. The comparative experiments indicate that the proposed method has a better performance than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction for rolling bearings encounters many challenges such as complex degradation processes, varying working conditions, and insufficient run-to-failure data. Transfer learning (TL), one paradigm of artificial intelligence technology, has demonstrated its powerful performance and great effectiveness for such challenges. As a result, many TL-based solutions have been widely developed and extensively studied for rolling bearing RUL prediction. Admittedly, several review articles have been published on RUL prediction. Nevertheless, the majority of these articles only concentrated on deep learning-based RUL prediction methods, and a review article that systematically overviews the status of TL-based RUL prediction has not been published. Therefore, it is urgent and significant to thoroughly summarize the academic publications and industrial applications related to TL-based RUL prediction, and present its potential challenges and future research directions. With such goals, the problem definitions of TL-based RUL prediction, the general procedure of RUL prediction, and typical TL-based RUL prediction algorithms are first introduced to help researchers quickly overview the state-of-the-art approaches and recent developments. Thereafter, relevant TL-based RUL prediction solutions are comprehensively discussed from the perspectives of three industrial scenarios, providing suggestions to researchers and engineers for selecting appropriate solutions in practical industrial applications. Finally, the key challenges and future trends in RUL prediction are presented to conclude this paper. We hope that this review of TL-based RUL prediction for rolling bearings can contribute to a better understanding of intelligent prognostic technology and will inspire researchers to extend their work on RUL prediction. [ABSTRACT FROM AUTHOR]

Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages of Deep Learning (DL) in feature representation, but also benefit from the superiority of Transfer Learning (TL) in knowledge transfer. As a result, DTL techniques can make DL-based fault diagnosis methods more reliable, robust and applicable, and they have been widely developed and investigated in the field of Intelligent Fault Diagnosis (IFD). Although several systematic and valuable review articles have been published on the topic of IFD, they summarized relevant research only from an algorithm perspective and overlooked practical applications in industry scenarios. Furthermore, a comprehensive review on DTL-based IFD methods is still lacking. From this insight, it is particularly important and more necessary to comprehensively survey the relevant publications of DTL-based IFD, which will help readers to conveniently understand the current state-of-the-art techniques and to quickly design an effective solution for solving IFD problems in practice. First, theoretical backgrounds of DTL are briefly introduced to present how the transfer learning techniques can be integrated with deep learning models. Then, major applications of DTL and their recent developments in the field of IFD are detailed and discussed. More importantly, suggestions on how to select DTL algorithms in practical applications, and some future challenges are shared. Finally, conclusions of this survey are given. At last, we have reason to believe that the works done in this article can provide convenience and inspiration for the researchers who want to devote their efforts in the progress and advance of IFD. [ABSTRACT FROM AUTHOR]