Article
Engineering, Multidisciplinary
Lei Guo, Jinrui Wang, Shan Wang, Baokun Han, Xiao Zhang, Zhenhao Yan, Meixia Jia
Summary: This paper proposes a modified general normalized sparse filtering (MGNSF) algorithm with strong noise adaptability for rotating machinery fault diagnosis. The algorithm does not require time-consuming denoising preprocessing and can extract the features of a faulty bearing under strong noise interference, demonstrating strong noise adaptability.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Daoguang Yang, Hamid Reza Karimi, Len Gelman
Summary: This paper proposes an explainable intelligence fault diagnosis framework that uses short-time Fourier transformation to recognize fault signals and visualizes the features learned by the model using a post hoc explanation method. The experimental results show that the framework achieves 100% testing accuracy and interpretability.
Article
Computer Science, Artificial Intelligence
Wenqing Wan, Jinglong Chen, Zitong Zhou, Zhen Shi
Summary: Fault diagnosis is crucial for the security of rotating machinery operations. This article proposes a self-supervised simple Siamese framework based on the contrastive learning algorithm for bearing fault diagnosis. The framework learns invariant characteristics of fault samples by maximizing the similarity between two views of each inputted sample. After fine-tuning with a small subset of labeled data, the network achieves satisfactory performance in bearing fault diagnosis.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Cai Yi, Hao Wang, Qiuyang Zhou, Qiwei Hu, Pengcheng Zhou, Jianhui Lin
Summary: In this article, an adaptive harmonic product spectrum (AHPS) method is proposed, which achieves adaptive frequency band segmentation based on signal power spectral density (PSD) by iteratively convolving with the Gaussian kernel function. The optimal resonance band is determined by maximizing the harmonic saliency index (HSI), resulting in improved accuracy in fault resonance band location.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Haixin Lv, Qian Liu, Jinglong Chen, Shuilong He, Enyong Xu, Tianci Zhang, Zitong Zhou
Summary: Data-driven methods, especially deep neural networks, have gained attention in machinery fault diagnosis. However, using raw machinery signals directly as input has limitations. This study proposes a Multi-Frequency Augmentation framework to improve model generalization and capture multi-frequency information for more effective fault diagnosis.
Article
Engineering, Electrical & Electronic
Xiaoyin Nie, Gang Xie
Summary: This article proposes a two-stage fault diagnosis framework to transform fault diagnosis with noisy labels into a semi-supervised learning procedure. It utilizes a backbone network with convolutional gated recurrent unit to extract temporal and spatial information, and introduces regularization terms and correction labels in the second stage to optimize network parameters. The experiments show that this framework significantly outperforms other state-of-the-art frameworks in both synthetic and real-world noisy label conditions.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Chemistry, Analytical
Widagdo Purbowaskito, Chen-Yang Lan, Kenny Fuh
Summary: A novel model-based fault detection and identification framework for induction motor-driven rotating machinery is proposed in this study, utilizing a data-driven approach to extract the state-space model and improve frequency-domain fault detection and identification by replacing current signals with residual signals. Experimental results show that the proposed method is more sensitive to fault signatures and can successfully identify mathematically unknown fault signatures.
Article
Engineering, Electrical & Electronic
Changbo He, Yujie Cao, Yang Yang, Yongbin Liu, Xianzeng Liu, Zheng Cao
Summary: In this article, a multidimensional normalized ResNet model is proposed for fault diagnosis of cross-working conditions under limited labeled samples. The model preprocesses the collected vibration data under different conditions using computed order tracking and enhances the feature extraction ability of ResNet by fusing batch normalization and group normalization. Moreover, it improves the robustness of the trained model by replacing the rectified linear unit with the Gaussian error linear unit.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Aerospace
Zongzhen Zhang, Shunming Li, Jiantao Lu, Yu Xin, Huijie Ma
Summary: The proposed method of Intrinsic Component Filtering (ICF) shows superior performance in fault diagnosis of rotating machinery, including feature learning, automatic fault classification, and weak fault component extraction. ICF can also be used as a filter training method to extract and separate weak fault signals.
CHINESE JOURNAL OF AERONAUTICS
(2021)
Article
Computer Science, Artificial Intelligence
Zidong Yu, Changhe Zhang, Jie Liu, Chao Deng
Summary: In this study, a novel time-scale adaptive CNN (TSACNN) and neural network denoiser (NND) are constructed based on a selective kernel block (SKB), and a fault diagnosis (FD) framework with strong generalization and anti-noise abilities is established through their deep fusion. Experimental results show that TSACNN can achieve better performance than four state-of-the-art CNN-based FD models under both noise interference and complex working conditions, and SKND can achieve better denoising ability than a state-of-the-art NND model. By deep fusion of SKND and TSACNN, the proposed SKND-TSACNN can achieve satisfactory FD accuracy and robustness under complex working conditions and strong noise interference.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Mechanical
Meng Rao, Ming J. Zuo, Zhigang Tian
Summary: This paper proposes a new deep learning model called speed normalized autoencoder (SN-AE) which removes the effects of speed variations for fault detection by normalizing the vibration signal. Case studies show that SN-AE achieves significantly better detection performances than existing AE-based fault detection methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Physics, Multidisciplinary
Qiyang Xiao, Sen Li, Lin Zhou, Wentao Shi
Summary: This paper proposes an intelligent diagnosis method for rotating machinery faults using improved variational mode decomposition and convolutional neural network to process non-stationary signals. The method automatically optimizes the number of modes and extracts time-domain features for fault diagnosis. The decomposed signal components are analyzed and correlated, and the high correlated components are selected to reconstruct the original signal. The method utilizes the continuous wavelet transform to extract two-dimensional time-frequency domain features, which are then applied to a convolutional neural network for fault feature identification.
Article
Engineering, Mechanical
Shun Wang, Yongbo Li, Khandaker Noman, Dong Wang, Ke Feng, Zheng Liu, Zichen Deng
Summary: The paper proposes a new entropy measure called cumulate spectrum distribution entropy (CSDEn), which can capture frequency-domain information of fault features. The method is evaluated using synthetic signals and experimental data, showing superior performance in detecting dynamic changes and measuring signal complexity compared to other methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Wei Liu, Yang Liu, Shuangxi Li
Summary: In this article, a novel method, DMSSST, is proposed to achieve a highly energy-concentrated time-frequency representation (TFR) with better robustness to noise and wide applicability.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Hanfang Dai, Yanxue Wang, Xuan Wang, Qi Liu
Summary: This paper applies the element analysis method to mechanical fault diagnosis for the first time and proposes a de-noising technique for rotating machinery signals based on the element analysis method. The method demonstrates excellent performance in signal characteristic extraction and fault diagnosis.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Fangyu Chen, Yongchang Wei, Hongchang Ji, Gangyan Xu
Summary: This paper introduces a dual-layer network analytical framework for evaluating standard systems in construction safety management and validates its effectiveness through a case study. The research findings suggest that key standards often encompass a wider array of risks, providing suggestions for revising construction standards.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Minghao Li, Qiubing Ren, Mingchao Li, Ting Kong, Heng Li, Huijing Tian, Shiyuan Liu
Summary: This study proposes a method using digital twin technology to construct a collision early warning system for marine piling. The system utilizes a five-dimensional model and four independently maintainable development modules to maximize its effectiveness. The pile positioning algorithm and collision early warning algorithm are capable of providing warnings for complex pile groups.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Seokhyun Ryu, Sungjoo Lee
Summary: This study proposes the use of patent information to develop a robust technology tree and applies it to the furniture manufacturing process. Through methods such as clustering analysis, semantic analysis, and association-rule mining, technological attributes and their relationships are extracted and analyzed. This approach provides meaningful information to improve the understanding of a target technology and supports research and development planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Shuai Ma, Kechen Song, Menghui Niu, Hongkun Tian, Yanyan Wang, Yunhui Yan
Summary: This paper proposes a feature-based domain disentanglement and randomization (FDDR) framework to improve the generalization of deep models in unseen datasets. The framework successfully addresses the appearance difference issue between training and test images by decomposing the defect image into domain-invariant structural features and domain-specific style features. It also utilizes randomly generated samples for training to further expand the training sample.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Fang Xu, Tianyu Zhou, Hengxu You, Jing Du
Summary: This study explores the impact of AR-based egocentric perspectives on indoor wayfinding performance. The results reveal that participants using the egocentric perspective demonstrate improved efficiency, reduced cognitive load, and enhanced spatial awareness in indoor navigation tasks.
ADVANCED ENGINEERING INFORMATICS
(2024)
Review
Computer Science, Artificial Intelligence
Yujie Lu, Shuo Wang, Sensen Fan, Jiahui Lu, Peixian Li, Pingbo Tang
Summary: Image-based 3D reconstruction plays a crucial role in civil engineering by bridging the gap between physical objects and as-built models. This study provides a comprehensive summary of the field over the past decade, highlighting its interdisciplinary nature and integration of various technologies such as photogrammetry, 3D point cloud analysis, semantic segmentation, and deep learning. The proposed 3D reconstruction knowledge framework outlines the essential elements, use phases, and reconstruction scales, and identifies eight future research directions. This review is valuable for scholars interested in the current state and future trends of image-based 3D reconstruction in civil engineering, particularly in relation to deep learning methods.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hang Zhang, Wenhu Wang, Shusheng Zhang, Yajun Zhang, Jingtao Zhou, Zhen Wang, Bo Huang, Rui Huang
Summary: This paper presents a novel framework for segmenting intersecting machining features using deep reinforcement learning. The framework enhances the effectiveness of intersecting machining feature segmentation by leveraging the robust feature representation, decision-making, and automatic learning capabilities of deep reinforcement learning. Experimental results demonstrate that the proposed approach successfully addresses some existing challenges faced by several state-of-the-art methods in intersecting machining feature segmentation.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Chao Zhao, Weiming Shen
Summary: This paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis, which enhances the model's generalization capabilities through synthesizing reliable samples and optimizing representations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ching-Chih Chang, Teng-Wen Chang, Hsin-Yi Huang, Shih-Ting Tsai
Summary: Ideation is the process of generating ideas through exploring visual and semantic stimuli for creative problem-solving. This process often requires changes in user goals and insights. Using pre-designed content and semantic-visual concepts for ideation can introduce uncertainty. An adaptive workflow is proposed in this study that involves extracting and summarizing semantic-visual features, using clusters of adapted information for multi-label classification, and constructing a design exploration model with visualization and exploration.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Zhen Wang, Shusheng Zhang, Hang Zhang, Yajun Zhang, Jiachen Liang, Rui Huang, Bo Huang
Summary: This research proposes a novel approach for machining feature process planning using graph convolutional neural networks. By representing part information with attribute graphs and constructing a learning model, the proposed method achieves higher accuracy and resolves current limitations in machining feature process planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hong-Wei Xu, Wei Qin, Jin-Hua Hu, Yan-Ning Sun, You -Long Lv, Jie Zhang
Summary: Wafer fabrication is a complex manufacturing system, where understanding the correlation between parameters is crucial for identifying the cause of wafer defects. This study proposes a Copula network deconvolution-based framework for separating direct correlations, which involves constructing a complex network correlation diagram and designing a nonlinear correlation metric model. The proposed method enables explainable fault detection by identifying direct correlations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yida Hong, Wenqiang Li, Chuanxiao Li, Hai Xiang, Sitong Ling
Summary: An adaptive push method based on feature transfer is proposed to address sparsity and cold start issues in product intelligent design. By constructing a collaborative filtering algorithm model and transforming the rating model, the method successfully alleviates data sparsity and cold start problems.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hairui Fang, Jialin An, Bo Sun, Dongsheng Chen, Jingyu Bai, Han Liu, Jiawei Xiang, Wenjie Bai, Dong Wang, Siyuan Fan, Chuanfei Hu, Fir Dunkin, Yingjie Wu
Summary: This work proposes a model for real-time fault diagnosis and distance localization on edge computing devices, achieving lightweight design and high accuracy in complex environments. It also demonstrates a high frame rate on edge computing devices, providing a novel solution for industrial practice.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yujun Jiao, Xukai Zhai, Luyajing Peng, Junkai Liu, Yang Liang, Zhishuai Yin
Summary: This paper proposes a digital twin-based motion forecasting framework that predicts the future trajectories of workers on construction sites, accurately predicting workers' motions in potential risk scenarios.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ling-Zhe Zhang, Xiang-Dong Huang, Yan-Kai Wang, Jia-Lin Qiao, Shao-Xu Song, Jian-Min Wang
Summary: Time-series DBMSs based on the LSM-tree have been widely applied in various scenarios. The characteristics of time-series data workload pose challenges to efficient queries. To address issues like query latency and inaccurate range, we propose a novel compaction algorithm called Time-Tiered Compaction.
ADVANCED ENGINEERING INFORMATICS
(2024)