Related references
Note: Only part of the references are listed.
Article
Computer Science, Artificial Intelligence
Alessia Amelio et al.
Summary: This paper presents an approach to map deep learning models into multilayer networks and applies it to the compression of Convolutional Neural Networks (CNN). Experimental results demonstrate the effectiveness of the approach in terms of accuracy and training time reduction.
COGNITIVE COMPUTATION
(2023)
Review
Engineering, Manufacturing
P. Nunes et al.
Summary: Predictive maintenance (PdM) uses sensor data and analytics techniques to optimize maintenance interventions, aiming to reduce costs and increase the competitiveness of enterprises. This paper focuses on the main challenges of implementing a generalized data-driven system for PdM, including noisy or erroneous sensor data, handling large volumes of data, and the lack of global approaches. It discusses the role of anomaly detection, prognostics methods, and architectures in PdM, and explores the latest trends, challenges, and opportunities in each perspective.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Dang Zhang et al.
Summary: Products for personalization require frequent dynamic interactions throughout the entire lifecycle. Researchers have proposed closed-loop product lifecycle management, but often neglect the technical implementation details. This article introduces a three-tier architecture using data and knowledge to address this issue and proposes FMECA automation and Bayesian network construction for efficient integration of design and maintenance phases. A proof-of-concept simulation demonstrates the applicability and efficiency of the proposed method, leading to closed-loop optimization of design and O&M services. This article has the potential to enable manufacturers to implement the linkage of design and O&M service business.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Anam Zaman et al.
Summary: This article proposes a continual learning methodology for point cloud registration, which improves the expressiveness and accuracy by utilizing the association learning and attention mechanism from previous point clouds. The methodology is evaluated on challenging benchmark datasets and shows remarkable performance improvement compared to existing methods, with exemplary generalization abilities to unseen datasets and new scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Virginia Fani et al.
Summary: Guaranteeing a wide range of products and fast delivery is a major challenge for industries today. This study proposes a framework that combines association rule mining and simulation to help companies identify production process issues and validate the results through data-driven simulation. The framework's key value lies in its ease of adaptability and iterative application for continuous improvement.
COMPUTERS IN INDUSTRY
(2023)
Article
Computer Science, Artificial Intelligence
Mingfei Liu et al.
Summary: This paper proposes a knowledge graph-based data representation approach for the Industrial Internet of Things (IIoT)-enabled cognitive manufacturing, and applies it in a Cyber-Physical Production System (CPPS) scenario. The proposed approach utilizes a perception-cognition dual system to analyze and make decisions in the resource allocation process, and demonstrates its advantages in handling dynamic demands through an illustrative example. This method provides a foundation for cognitive manufacturing in processing real-time industrial information.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Danilo Giordano et al.
Summary: Predictive maintenance is a growing field of interest, and in the automotive domain, the use of on-board sensors and cloud data transmission allows car manufacturers to deploy solutions that prevent component malfunctioning. This paper presents PREPIPE, a data-driven pipeline for predictive maintenance that predicts the clogging status of the oxygen sensor in diesel engines. PREPIPE explores various aspects such as signal selection, data collection, feature transformation, feature selection, and historical feature inclusion to optimize the prediction accuracy. The performance of PREPIPE is thoroughly evaluated and compared with deep learning architectures, showing its ability to identify critical situations before sensor failure while maintaining interpretability.
COMPUTERS IN INDUSTRY
(2022)
Article
Automation & Control Systems
Sunday Ochella et al.
Summary: Prognostics and health management (PHM) is crucial in engineering systems, particularly with the support of artificial intelligence (AI) and machine learning (ML) algorithms. Data-driven approaches are effective in identifying failure mechanisms and root causes using large-scale datasets. The application of AI technologies in Industry 4.0 has increased the need for more predictive and proactive maintenance practices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Industrial
Yuqian Lu et al.
Summary: This position paper discusses the concept, needs, reference model, enabling technologies, and system frameworks of human-centric manufacturing. It provides a relatable vision and research agenda for future work in human-centric manufacturing systems. Human-centric manufacturing should address human needs and the human-machine relationships will evolve.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Gianluca Bonifazi et al.
Summary: This paper proposed a framework that utilizes Sentient Multimedia Systems and Machine Learning to enhance workplace safety. It specifically focuses on fall detection, introducing a Machine Learning based wearable device designed, built, and tested for this purpose, as well as a safety coordination platform for monitoring the work environment and activating alarms in case of falls.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zixu Liu et al.
Summary: This paper presents the architectural design and implementation of DIGICOR, a collaborative Industry 4.0 (I4.0) platform that enables SMEs to form supply chain collaborations and address complex supply chain requests. The DIGICOR architecture is built on the event-driven service-oriented architecture (EDSOA) model, supporting collaboration between SMEs, dynamic modeling of their systems and services, and integration with the supply chains of large OEMs. It provides an open and extensible solution for creating a collaboration marketplace, planning and controlling collaborative production, logistics, and risk management, and seamless connectivity to automation solutions and real-time data sources.
COMPUTERS IN INDUSTRY
(2022)
Article
Computer Science, Interdisciplinary Applications
Alberto Olivares-Alarcos et al.
Summary: Industrial collaborative robots will play a role in unstructured scenarios and various tasks, collaborating with humans and adapting to unexpected situations. This article proposes an Ontology for Collaborative Robotics and Adaptation (OCRA), which ensures reliable human-robot collaboration and enhances the reusability of domain knowledge. The validations demonstrate the effectiveness of OCRA using competency questions and limit cases.
COMPUTERS IN INDUSTRY
(2022)
Article
Computer Science, Artificial Intelligence
Hong Xiao et al.
Summary: This paper proposes a health assessment and state prediction algorithm based on HMM and TCN for mechanical axis health management in industrial robot applications, achieving real-time accurate assessment and prediction of state transitions. The method demonstrates better accuracy and prediction indicators in experiments compared to other models.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Liang Zong et al.
Summary: Data transmission is crucial for industrial production in the Industry 5.0 era. This article proposes a satellite-supported transmission control scheme for industrial IoT that can ensure continuity and stability in emergency communication cases.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Mechanical
Vincentius Ewald et al.
Summary: The paper explores the application of predictive maintenance, structural health monitoring, and deep learning in aircraft maintenance, introducing a novel approach that combines structural health monitoring with deep learning to enhance the efficiency and accuracy of fault detection.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Interdisciplinary Applications
Qiushi Cao et al.
Summary: In the context of Industry 4.0, smart factories utilize advanced technologies for production monitoring and analysis, but the heterogeneous nature of industrial data leads to complex knowledge extraction. In order to achieve predictive maintenance, symbolic AI technologies are required. KSPMI is a knowledge-based system developed based on a hybrid approach utilizing both statistical and symbolic AI technologies.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Artificial Intelligence
Aniello De Santo et al.
Summary: Predictive Maintenance is crucial for minimizing downtime and fault rate in modern industrial scenarios. Machine learning and deep learning approaches show promise for accurate predictions, but their data-heavy requirements limit their real-world applications. This paper proposes a framework to evaluate time series encoding techniques applied to image classifiers for predictive maintenance tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Engineering, Industrial
Liqiao Xia et al.
Summary: This paper introduces the application of graph-based approaches in Predictive Maintenance (PdM), focusing on the stages of anomaly detection, diagnosis, prognosis, and maintenance decision-making. By combining graph construction methods and cognitive intelligence, graph-based approaches can achieve semantic causal inference, heterogeneous association, and visualized explanation in PdM, which shows promising performance.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Engineering, Industrial
Liqiao Xia et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Multidisciplinary
Qibo Yang et al.
Summary: This paper presents a methodology for fault prognosis of industrial robots, including modeling approach using domain generalization-adversarial long short-term memory, two-stage health assessment based on principal component analysis-squared prediction error and p-chart, and a workflow containing feature extraction, smoothing, normalization, and selection. The case study demonstrates the methodology effectively reduces variations and improves prediction of Remaining Useful Life (RUL).
Article
Computer Science, Interdisciplinary Applications
Ping Zhang et al.
Summary: This study develops a model-based reinforcement learning approach for maintenance optimization, which can be applied to situations where the degradation process is unknown or hard to determine. By customizing Q-learning method and Dyna-Q method to optimize assessment values, the final maintenance policy is obtained effectively.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Mechanical
Jun Yu et al.
Summary: The complex and non-stationary nature of vibration signals from planet bearings, along with limited training samples, poses challenges for predicting remaining useful life (RUL). A method combining C-DRGAN and AD is proposed to address these issues, effectively extracting fault features and predicting RUL under varying conditions with small samples. The method's adaptability to nonlinear and non-stationary signals is validated through experimental analyses.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Haiying Liu et al.
Summary: In this paper, a fault diagnosis approach for mechanical equipment is proposed, which combines one-dimensional CNN, GRU, attention mechanism, and knowledge graph. The proposed ATT-1D CNN-GRU model is trained on rolling bearing datasets under different loads, achieving an accuracy of up to 99%.
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
(2021)
Review
Computer Science, Hardware & Architecture
Claudio Gutierrez et al.
COMMUNICATIONS OF THE ACM
(2021)
Article
Computer Science, Interdisciplinary Applications
P. Aivaliotis et al.
Summary: This paper introduces a generic framework to enhance advanced physics-based models by combining them with degradation curves of robot components in order to predict the robot's future behavior and estimate its Remaining Useful Life. The Digital Twin concept is employed to monitor the robot's health status to ensure convergence between simulated and actual behaviors.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Engineering, Mechanical
Shuai Yang et al.
Summary: A two-dimensional convolutional neural network is proposed in this paper for the fault diagnosis of reducer on industrial robots, which can automatically extract features and reduce the connections between neurons and parameters, improving the efficiency of network training and demonstrating accuracy through experimental validation.
STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Kihoon Lee et al.
Summary: Transfer learning can improve the diagnostic performance of the target domain when dealing with large domain discrepancies, but may lead to negative transfer effects in cases of significant discrepancies. A multi-objective instance weighting-based transfer learning network has been proposed and successfully applied to fault diagnosis, which adjusts the influence of domain data on model training and maximizes the performance of transfer learning.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
Xiaofeng Yuan et al.
Summary: An LSTM network with spatiotemporal attention is proposed for soft sensor modeling in industrial processes, improving prediction performance by identifying important input variables related to the quality variable and discovering quality-related hidden states adaptively.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Computer Science, Theory & Methods
Aidan Hogan et al.
Summary: This article provides a comprehensive introduction to knowledge graphs, covering their popularity in industry and academia, graph-based data models, query and validation languages, and techniques for knowledge representation and extraction. It also outlines high-level future research directions for knowledge graphs.
ACM COMPUTING SURVEYS
(2021)
Article
Chemistry, Multidisciplinary
Zhiyang He et al.
Summary: The development of industrial robots and mechanical equipment has led to increasingly complex mechanical systems, posing a challenge for condition monitoring. Research has found that low rank is a common feature of rotating machinery vibration source signals, leading to the proposal of a multi-low-rank constrained vibration source signal separation method. This method has been shown to outperform other techniques in terms of clustering results and signal-to-signal ratio values.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Changdae Lee et al.
Summary: The manufacturing industry is undergoing a revolution with smart manufacturing and Industry 4.0, yet facing challenges in real-time communication, interoperability, and connectivity between the internet and industrial devices. By referring to the ISO/IEC 30141 standard, a four-domain integrated architecture was proposed and validated through the construction of a testbed for the manufacturing system.
Article
Engineering, Electrical & Electronic
Hai Yang et al.
Summary: This article introduces a method to improve the accuracy of U-tube CMF under pulsating flow, based on variable step-size LMS filter and Hilbert transform. Experimental results show that the stability and accuracy of the proposed algorithm are better than that of the traditional CMF phase difference calibration method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Information Systems
Rohitash Chandra et al.
Summary: This study evaluates the performance of deep learning models for multi-step ahead time series prediction, and finds that bidirectional and encoder-decoder LSTM networks show the best accuracy in given time series problems.
Review
Computer Science, Information Systems
Georg Buchgeher et al.
Summary: Knowledge graphs in manufacturing and production can enhance production efficiency and quality output, aiding companies in achieving Industry 4.0 goals. However, further research is needed as existing studies in the field are still in early stages, with gaps in understanding how knowledge graphs can be effectively applied in manufacturing and production.
Article
Computer Science, Information Systems
Changchun Liu et al.
Summary: This paper proposes a new predictive maintenance method based on improved deep adversarial learning, which can effectively solve the economic loss caused by machine faults in intelligent manufacturing systems, improve the accuracy and efficiency of fault prediction, reduce maintenance costs and downtime, and extend the life of machines.
Article
Computer Science, Artificial Intelligence
Li Li et al.
APPLIED INTELLIGENCE
(2020)
Article
Engineering, Electrical & Electronic
Ke Lv et al.
IET ELECTRIC POWER APPLICATIONS
(2020)
Article
Automation & Control Systems
Olga Fink et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2020)
Article
Engineering, Industrial
Bin Bai et al.
INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION
(2020)
Article
Automation & Control Systems
Aniekan Essien et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Review
Computer Science, Interdisciplinary Applications
Jovani Dalzochio et al.
COMPUTERS IN INDUSTRY
(2020)
Article
Information Science & Library Science
Franck Ravat et al.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2020)
Review
Automation & Control Systems
Kai Zhong et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2020)
Article
Chemistry, Analytical
Jiung Huh et al.
Article
Engineering, Multidisciplinary
Weichao Yue et al.
Article
Automation & Control Systems
Haoyue Liu et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2019)
Article
Computer Science, Information Systems
Xianbin Sun et al.
Article
Chemistry, Analytical
Donghyun Park et al.
Article
Automation & Control Systems
Chunhui Zhao et al.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2017)
Article
Computer Science, Artificial Intelligence
Quan Wang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2017)
Editorial Material
Multidisciplinary Sciences
Andrew Kusiak
Article
Computer Science, Interdisciplinary Applications
Wang Xiaoqiao et al.
COMPUTERS IN INDUSTRY
(2015)
Article
Computer Science, Artificial Intelligence
Wei-Ming Wang et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2014)
Article
Computer Science, Artificial Intelligence
Bernard Kamsu-Foguem et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2013)
Article
Automation & Control Systems
Xiaodong Yu et al.
CONTROL ENGINEERING PRACTICE
(2012)
Article
Management
Mohamed Khalifa et al.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2008)