Article
Chemistry, Multidisciplinary
Wieger Tiddens, Jan Braaksma, Tiedo Tinga
Summary: This paper proposes a framework to support asset owners in selecting the optimal predictive maintenance method for their situation. The framework includes classifications of ambition levels, data types, and types of predictive maintenance methods. Four industrial case studies were used to test and demonstrate the effectiveness of the framework.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Interdisciplinary Applications
Venkat Nemani, Austin Bray, Adam Thelen, Chao Hu, Steve Daining
Summary: The key to accurate remaining useful life prediction in prognostics and health management is finding a signal that quantifies the health status of a physical asset. In response to challenges, new trend metrics and probabilistic comparison tools are proposed to accurately quantify signal monotonicity and compare health index performance across different units.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Review
Engineering, Manufacturing
P. Nunes, J. Santos, E. Rocha
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
Engineering, Multidisciplinary
Yuxin Wen, Md Fashiar Rahman, Honglun Xu, Tzu-Liang Bill Tseng
Summary: This paper extensively reviews recent advances and trends in data-driven machine prognostics, focusing on their practical applications. It categorizes existing literature, reports the latest research progress, methodology, and investigates the diverse fields of machine prognostics applications. Additionally, it discusses the challenges, opportunities, and future trends of predictive maintenance.
Article
Engineering, Industrial
Ghita Bencheikh, Agnes Letouzey, Xavier Desforges
Summary: The Industry 4.0 paradigm improves the agility of productive organizations through the deployment of cutting-edge technologies. Prognostic and Health Management (PHM) is a service that contributes to the health assessment of manufacturing resources and provides decision supports for production and predictive maintenance management. The proposed multi-agent system SCEMP allows the simultaneous scheduling of production tasks and predictive maintenance activities by compromising on their objectives. It is a flexible framework that can be adapted to various manufacturing situations and can be used to assess the interest of implementing prognostic functions for machine components.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Energy & Fuels
Mingjiang Xie, Jianli Zhao, Ming J. Zuo, Zhigang Tian, Libin Liu, Jinming Wu
Summary: This study establishes a multi-objective optimization framework that considers the structure, availability, and multi-stage corrosion of pipeline systems. By combining with the general structure of pipeline systems, chromosome coding is designed to improve the efficiency of the evolutionary algorithm. The proposed methodology demonstrates effectiveness and accuracy in pipeline systems with different structures and parameters.
Article
Automation & Control Systems
Arnav Malawade, Nathan D. Costa, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad A. Al Faruque
Summary: Using hierarchical temporal memory (HTM) for online real-time anomaly detection can preemptively detect bearing failures and simulated 3-D printer failures more effectively, outperforming state-of-the-art algorithms.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Industrial
Juseong Lee, Mihaela Mitici
Summary: This study proposes a framework that integrates data-driven probabilistic Remaining-Useful-Life (RUL) prognostics with predictive maintenance planning, using aircraft turbofan engines as an example. By employing this framework, the total maintenance cost can be reduced, unscheduled maintenance can be prevented, and the wasted life of engines can be limited.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Review
Chemistry, Analytical
Marek Moleda, Bozena Malysiak-Mrozek, Weiping Ding, Vaidy Sunderam, Dariusz Mrozek
Summary: Proper maintenance of industrial equipment is essential for the stability and health of production systems. In industries like electrical power, equipment failures may result in high costs and substantial economic losses. Therefore, the power production industry uses various approaches to maintenance, including traditional solutions and AI-based analytics, to prevent downtimes and ensure efficient operations.
Article
Engineering, Industrial
Ingeborg de Pater, Arthur Reijns, Mihaela Mitici
Summary: This paper proposes a dynamic, predictive maintenance scheduling framework that takes into account imperfect Remaining Useful Life (RUL) prognostics. Maintenance tasks are scheduled based on periodically updated RUL prognostics and alarms triggered by their evolution over time. A safety factor is used to avoid component failures in the presence of potential errors in the RUL prognostics.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Review
Chemistry, Analytical
Michael J. Scott, Wim J. C. Verhagen, Marie T. Bieber, Pier Marzocca
Summary: In recent years, the use of sensor technologies and digitalization in aircraft sustainment and operations has enhanced the ability to detect, diagnose, and predict the health of aircraft. Predictive maintenance and related concepts have gained increasing attention in research, but limitations still exist in terms of research methodology definition and the lack of review papers on military applications. This review paper aims to address these gaps by providing a systematic review focused on the operations and sustainment of fixed-wing defence aircraft.
Article
Engineering, Multidisciplinary
Chuang Chen, Cunsong Wang, Ningyun Lu, Bin Jiang, Yin Xing
Summary: This paper presents a novel data-driven predictive maintenance strategy that achieves accurate failure prognosis through degradation feature selection and degradation prognostic modeling modules, outperforming traditional maintenance strategies.
EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
(2021)
Article
Computer Science, Artificial Intelligence
Chuang Chen, Jiantao Shi, Ningyun Lu, Zheng Hong Zhu, Bin Jiang
Summary: This paper proposes a novel data-driven predictive maintenance strategy, which includes a Local Uncertainty Estimation (LUE) model and a Maintenance Cost Rate (MCR) function, to address the separate and hierarchical tasks of RUL prediction and maintenance decision-making. The strategy is validated in the field of aero-engine health monitoring and shows promising results in reducing system maintenance costs.
Article
Computer Science, Interdisciplinary Applications
Bernar Tasci, Ammar Omar, Serkan Ayvaz
Summary: This study proposes a machine learning-based predictive maintenance approach to predict the Remaining Useful Life of production lines in manufacturing. By using data from integrated IoT sensors in a real-world factory, the approach aims to predict potential equipment failures on assembly lines in real-time and prevent downtime, resulting in resource savings.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Automation & Control Systems
Alberto Jaenal, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez
Summary: This paper presents a general deep learning architecture, MachNet, that addresses the heterogeneity of Industry 4.0-PdM solutions and is capable of handling various PdM problems. The modular architecture allows for an arbitrary number and type of sensors, and the integration of prior information. Experimental results show that MachNet achieves excellent performance in health state and remaining useful life estimation.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
