Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults

Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to entire industrial applications. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status. However, the complex working conditions of rolling bearings often make the fault-related information easily buried in noise and other interference. Therefore, it is challenging for existing approaches to extract sufficient critical features in these scenarios. To address this issue, this paper proposes a novel CNN-Transformer network, referred to as Dconformer, capable of extracting both local and global discriminative features from noisy vibration signals. The main contributions of this research include: (1) Developing a novel joint-learning strategy that simultaneously enhances the performance of signal denoising and fault diagnosis, leading to robust and accurate diagnostic results; (2) Constructing a novel CNN-transformer network with a multi-branch cross-cascaded architecture, which inherits the strengths of CNNs and transformers and demonstrates superior anti-interference capability. Extensive experimental results reveal that the proposed Dconformer outperforms five state-of-the-art approaches, particularly in strong noisy scenarios

Noise-Boosted Convolutional Neural Network for Edge-based Motor Fault Diagnosis with Limited Samples

Convolutional neural networks (CNNs) have been widely applied in motor fault diagnosis. However, to obtain high recognition accuracy, massive training data are typically required and transmitted to the cloud/local server for training, which may suffer from security and privacy problems. In this study, a noise-boosted CNN (NBCNN) model is developed to achieve accelerated training and improved recognition accuracy with limited training samples. First, the NBCNN model with a noise-injection fully connected layer is established. Then, a strategy for noise selection and injection is proposed to obtain an optimal matching among the data, model, and noise. Finally, the optimal injected noise accelerates the convergence of model training and improves the accuracy of motor fault diagnosis. Compared with the conventional CNN without noise injection and the state-of-the-art models, the effectiveness and superiority of the proposed NBCNN model are validated by two benchmark datasets. In addition, the algorithm is deployed onto an edge device and the results show that the training speed of the developed NBCNN can reach nine times faster than the conventional CNN. The proposed method shows remarkable potential for distributed model training, federal learning, and real-time motor fault diagnosis

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance