相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Engineering, Industrial
Chong Chen et al.
Summary: The accurate diagnosis of compound faults in industrial robots is crucial for maintenance management. However, the complex and weak failure features in the noisy working environment pose a major challenge. In this study, a compact Transformer network approach is proposed to overcome these challenges and achieve accurate compound fault diagnosis.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Engineering, Industrial
Yiming He et al.
Summary: In this paper, an in-situ fault diagnosis method via the multi-scale mixed convolutional neural networks (MSMCNN) model is proposed for harmonic reducers. The MSMCNN model extracts more comprehensive and complementary fault features from complex in-situ multi-channel signals with industrial noise. Experimental results show that the MSMCNN achieves superior diagnosis accuracy compared to classical and some state-of-the-art DL methods.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ruiyi Ma et al.
Summary: A meta learning intelligent fault diagnosis method is proposed to address the problem of new faults not being identified due to lack of training data in the process of equipment operation. The method utilizes multi-scale dilated convolution and relation module for feature extraction and fault diagnosis. The training set is transformed into multiple tasks using meta learning strategy to train the proposed method, which is validated through bearing and gearbox experiments.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Mechanical
Jianyu Long et al.
Summary: This article proposes a fault diagnosis method for industrial robots based on an attitude sensor and a multiscale convolutional capsule network (MCCN). By monitoring the attitude of transmission components, fault features are learned from attitude data, and effective fault diagnosis is achieved by fusing multiscale features and spatial-relational features.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Xing Zhou et al.
Summary: In this study, a compound fault diagnosis algorithm for an industrial robot based on multi-modal feature extraction and fusion is proposed. By adopting the multi-head self-attention enhanced convolution neural network module and long short-term memory network module, fault-related features are learned from different perspectives simultaneously, and the local and global features are fused for accurate compound fault diagnosis.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Mechanical
Chao Zhao et al.
Summary: This study proposes a semi-supervised domain generalization fault diagnosis (Sem-iDGFD) method, which assigns reliable pseudo labels to unlabeled data with knowledge assistance from labeled data. An entropy-based sample purification mechanism is designed to improve the quality of the pseudo-labeled samples. Experimental results demonstrate that the proposed method achieves higher precision than other common SemiDGFD methods and comparable performance with up-to-date fully-labeled DGFD methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Interdisciplinary Applications
Yiming He et al.
Summary: This paper proposes an in-situ fault diagnosis method using the multi-stage residual fusion convolution neural networks (MSRFCNN) model for detecting faults in the spindle motor of CNC machines. Experimental results show that the MSRFCNN outperforms classical and some state-of-the-art DL methods.
COMPUTERS IN INDUSTRY
(2023)
Article
Engineering, Industrial
Yiming Xiao et al.
Summary: To ensure researchers trust deep diagnostic models, interpretable rotating machinery fault diagnosis (RMFD) research has been developed. However, there is limited work on quantifying uncertainty in results and explaining its sources and composition. This paper proposes a Bayesian variational learning method to introduce uncertainty into the attention weights of Transformer and constructs a probabilistic Bayesian Transformer for trustworthy RMFD. By inferring prior and variational posterior distributions of attention weights, uncertainty is perceived, and an uncertainty quantification and decomposition scheme is developed to achieve confidence characterization of results and separation of epistemic and aleatoric uncertainty. The proposed method is validated in three out-of-distribution generalization scenarios.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Engineering, Mechanical
Xingkai Chen et al.
Summary: Most of the existing research on unsupervised cross-domain intelligent fault diagnosis focuses on single-source domain adaptation, lacking the ability to utilize multiple source domains with diverse diagnostic information. This paper proposes a dual adversarial guided unsupervised multi-domain adaptation network (DAG-MDAN) to better extract common features and integrate multi-source domain knowledge. The DAG-MDAN includes an edge adversarial module (EA-Module) and an inner adversarial module (IA-Module) to enhance domain confusion and a multi-subnet collaborative decision module (MCD-Module) for better fusion decisions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Interdisciplinary Applications
Ziqiang Pu et al.
Summary: Transmission systems of industrial robots are prone to failures due to harsh operating environments. In this study, a generative adversarial one-shot diagnosis (GAOSD) approach is proposed to diagnose robot transmission faults with only one sample per faulty pattern. Experimental results show that the proposed GAOSD has promising performance on the fault diagnosis of robot transmission systems.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Engineering, Electrical & Electronic
Zhuo Zhi et al.
Summary: This paper proposes a method for identifying faults in harmonic reducers using the joint wavelet regional correlation threshold denoising algorithm and the convolutional neural network-long short term memory fault detection method, which significantly improves the accuracy of fault detection.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Interdisciplinary Applications
Lerui Chen et al.
Summary: A novel CNN model is proposed to combine spectrum calculation and fault diagnosis functions, optimizing parameters to achieve nonlinear spectrum calculation for feature extraction and diagnosis. Experimental results demonstrate the best performance of the model on heavy-duty industrial robot systems.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Interdisciplinary Applications
Wei Wang et al.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Review
Engineering, Electrical & Electronic
Elias Rajaby et al.
Summary: Implementing discrete Fourier transform (DFT) requires high computational resources and time. Fast Fourier transform (FFT) algorithm has a lower computational complexity than DFT, but still faces challenges with big data. Sparse fast Fourier transform (SFFT) algorithms have been developed to reduce the computational complexity of Fourier transform.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Engineering, Industrial
Chao Zhao et al.
Summary: In this study, a novel open set domain adaptation network based on dual adversarial learning is proposed to address the challenges in cross-domain fault diagnosis. The method utilizes an auxiliary domain discriminator to assign similarity weights for individual target samples and employs weighted adversarial learning to selectively adapt domain distributions. Experimental results demonstrate the promising performance of the proposed method, outperforming existing state-of-the-art open set domain adaptation methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Computer Science, Interdisciplinary Applications
Shimin Liu et al.
Summary: Digital twin technology has been explored and applied in the machining process. This paper proposes an adaptive reconstruction method to enhance the adaptability of digital twin machining systems. The feasibility of this method is validated through experiments.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Engineering, Mechanical
Chao Zhao et al.
Summary: This paper proposes a novel domain generalization network for fault diagnosis under unknown working conditions, which can exploit domain invariance and retain domain specificity simultaneously. It effectively tackles the problem of target data inaccessibility in real-time cross-domain fault diagnosis.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Zhou Xing et al.
Summary: This paper proposes a fault diagnosis method for the harmonic reducer in industrial robots based on deep learning. By using consecutive time-domain vibration signals and a 1-dimensional convolutional neural network with matrix kernels, accurate fault diagnosis for the harmonic reducer can be achieved in industrial robots.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Automation & Control Systems
Zheng Chai et al.
Summary: This article introduces a fault-prototypical adapted network for cross-domain industrial intelligent fault diagnosis using deep transfer learning. Experimental results show that the proposed approach learns transferable feature representations that reduce domain discrepancy and improve diagnosis performance on target data.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Industrial
Chao Zhao et al.
Summary: This paper proposes an adaptive open set domain generalization network to diagnose unknown faults under unknown working conditions. By implementing a local class cluster module and an outlier detection module, the method is able to effectively diagnose faults in the presence of unknown fault modes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Multidisciplinary
Yanrui Jin et al.
Summary: This paper introduces a novel deep learning method for compound fault diagnosis, achieving high accuracy through a decoupling attentional residual network, multi-label decoupling classifier, and active learning approach. The method can reach the same accuracy with a small number of compound fault samples as using a large number of samples, reducing the labeling workload for domain experts.
Article
Computer Science, Interdisciplinary Applications
Haifeng Wang et al.
Summary: Solder paste printing is a critical procedure in SMT assembly lines, influenced by various factors that affect the printing results dynamically. This research utilizes neural networks and wavelet filtering technology to predict printing performance, effectively reducing defects and controlling cleaning frequency.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Mohamed Marei et al.
Summary: By utilizing a transfer learning enabled CNN approach, this study effectively predicts and evaluates the wear condition of cutting tools, providing viable strategies for health management in CNC machining applications.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Artificial Intelligence
Bo Zhao et al.
Article
Engineering, Electrical & Electronic
Hui Chen et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2020)
Article
Engineering, Electrical & Electronic
Jiangxin Yang et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2020)
Article
Chemistry, Multidisciplinary
Zilong Zhuang et al.
APPLIED SCIENCES-BASEL
(2019)
Article
Computer Science, Interdisciplinary Applications
Pengfei Liang et al.
COMPUTERS IN INDUSTRY
(2019)
Article
Engineering, Mechanical
Wei Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2018)
Article
Chemistry, Analytical
Wei Zhang et al.
Review
Engineering, Mechanical
Wade A. Smith et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2015)
