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Wu F, Wu Q, Tan Y, Xu X. Remaining Useful Life Prediction Based on Deep Learning: A Survey. SENSORS (BASEL, SWITZERLAND) 2024; 24:3454. [PMID: 38894245 PMCID: PMC11174398 DOI: 10.3390/s24113454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024]
Abstract
Remaining useful life (RUL) is a metric of health state for essential equipment. It plays a significant role in health management. However, RUL is often random and unknown. One type of physics-based method builds a mathematical model for RUL using prior principles, but this is a tough task in real-world applications. Another type of method estimates RUL from available information through condition and health monitoring; this is known as the data-driven method. Traditional data-driven methods require significant human effort in designing health features to represent performance degradation, yet the prediction accuracy is limited. With breakthroughs in various application scenarios in recent years, deep learning techniques provide new insights into this problem. Over the past few years, deep-learning-based RUL prediction has attracted increasing attention from the academic community. Therefore, it is necessary to conduct a survey on deep-learning-based RUL prediction. To ensure a comprehensive survey, the literature is reviewed from three dimensions. Firstly, a unified framework is proposed for deep-learning-based RUL prediction and the models and approaches in the literature are reviewed under this framework. Secondly, detailed estimation processes are compared from the perspective of different deep learning models. Thirdly, the literature is examined from the perspective of specific problems, such as scenarios where the collected data consist of limited labeled data. Finally, the main challenges and future directions are summarized.
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Affiliation(s)
- Fuhui Wu
- School of Information Engineering, Wuhan College, Wuhan 430212, China
| | - Qingbo Wu
- College of Computer, National University of Defense Technology, Changsha 410073, China
| | - Yusong Tan
- College of Computer, National University of Defense Technology, Changsha 410073, China
| | - Xinghua Xu
- National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan 430033, China
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Scott MJ, Verhagen WJC, Bieber MT, Marzocca P. A Systematic Literature Review of Predictive Maintenance for Defence Fixed-Wing Aircraft Sustainment and Operations. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22187070. [PMID: 36146419 PMCID: PMC9502349 DOI: 10.3390/s22187070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/10/2022] [Accepted: 09/15/2022] [Indexed: 05/27/2023]
Abstract
In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available.
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Affiliation(s)
- Michael J. Scott
- School of Engineering (Aerospace Engineering and Aviation), Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC 3000, Australia
| | - Wim J. C. Verhagen
- School of Engineering (Aerospace Engineering and Aviation), Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC 3000, Australia
| | - Marie T. Bieber
- Air Transport & Operations, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands
| | - Pier Marzocca
- School of Engineering (Aerospace Engineering and Aviation), Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC 3000, Australia
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A Study of the Impact of Predictive Maintenance Parameters on the Improvment of System Monitoring. MATHEMATICS 2022. [DOI: 10.3390/math10132153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Predictive maintenance can be efficiently improved by studying the sensitivity of the maintenance decisions with respect to changes in the proposed model parameters (costs, duration of reparation, etc.). To address this issue, we first propose an original approach that includes both maintenance costs and maintenance risks in the same objective function to minimize. This approach uses the RUL as an indicator of the health state of the system and supposes that the system is under regular inspections and can only be replaced by a new system in case of serious deterioration or failure. Then, we present a process of human decision making under uncertainty based on several criteria. Finally, we study and analyze the influence of the model parameters and their implications on the obtained maintenance policies. The study will lead to some recommendations that can improve the predictive maintenance decisions and help experts better handle maintenance costs.
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Condition-Based Monitoring and Maintenance: State of the Art Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020688] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Manufacturing firms face great pressure to reduce downtime as well as maintenance costs. Condition-based maintenance (CBM) can be used to effectively manage operations and maintenance by monitoring detailed machine health information. CBM policies and the development of the mathematical models have been growing recently. This paper provides a review of the theoretical and practical development in the field of condition-based maintenance and its current advancements. Standard CBM platform could make it effective and efficient in implementation and performance improvement.
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Hoffmann MW, Wildermuth S, Gitzel R, Boyaci A, Gebhardt J, Kaul H, Amihai I, Forg B, Suriyah M, Leibfried T, Stich V, Hicking J, Bremer M, Kaminski L, Beverungen D, zur Heiden P, Tornede T. Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2099. [PMID: 32276442 PMCID: PMC7181000 DOI: 10.3390/s20072099] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 11/16/2022]
Abstract
The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.
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Affiliation(s)
| | | | - Ralf Gitzel
- ABB AG, Corporate Research Germany, 68526 Ladenburg, Germany
| | - Aydin Boyaci
- ABB AG, Corporate Research Germany, 68526 Ladenburg, Germany
| | - Jörg Gebhardt
- ABB AG, Corporate Research Germany, 68526 Ladenburg, Germany
| | - Holger Kaul
- ABB AG, Corporate Research Germany, 68526 Ladenburg, Germany
| | - Ido Amihai
- ABB AG, Corporate Research Germany, 68526 Ladenburg, Germany
| | - Bodo Forg
- Heimann Sensor GmbH, 01109 Dresden, Germany
| | - Michael Suriyah
- Institute of Electric Energy Systems and High Voltage Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Thomas Leibfried
- Institute of Electric Energy Systems and High Voltage Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Volker Stich
- FIR (Institute for Industrial Management) at the RWTH Aachen University, 52074Aachen, Germany
| | - Jan Hicking
- FIR (Institute for Industrial Management) at the RWTH Aachen University, 52074Aachen, Germany
| | - Martin Bremer
- FIR (Institute for Industrial Management) at the RWTH Aachen University, 52074Aachen, Germany
| | - Lars Kaminski
- FIR (Institute for Industrial Management) at the RWTH Aachen University, 52074Aachen, Germany
| | - Daniel Beverungen
- Chair of Business Information Systems, Paderborn University, 33098 Paderborn, Germany
| | - Philipp zur Heiden
- Chair of Business Information Systems, Paderborn University, 33098 Paderborn, Germany
| | - Tanja Tornede
- Software Innovation Campus Paderborn, Department of Computer Science and Heinz Nixdorf Institute, Paderborn University, 33098 Paderborn, Germany
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Integration of Prognostics and Control of an Oil/CO2 Subsea Separation System. Processes (Basel) 2020. [DOI: 10.3390/pr8020148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The exploitation of reserves with a high CO2 content is challenging because of the need for its separation and the environmental impact associated with its generation. In this context, a suitable use for the generated CO2 is its reinjection into the reservoir, and subsea CO2 separation improves the efficiency of this process. The main objective of this work is to investigate the health-aware control of a subsea CO2 separation system. Previously identified linear models were used in a predictive controller with Kalman filter-based state estimation and online model update, and simulations were performed to evaluate the controller tuning. Regarding prognostics, a stochastic model of pump degradation, sensitive to its operating conditions, was proposed, and a particle filter was implemented to perform online degradation state estimation and remaining useful lifetime prediction. Finally, a health-aware controller was designed, which could extend the life of the process by four months when compared to operation with a conventional model predictive controller. Some difficulties in combining reference tracking and lifetime extension objectives were also investigated. The obtained results indicate that dealing with the control problem through the multiobjective optimization theory or addressing the lifetime extension in an optimization layer may improve its performance.
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Teixeira ELS, Tjahjono B, Alfaro SCA. A novel framework to link Prognostics and Health Management and Product–Service Systems using online simulation. COMPUT IND 2012. [DOI: 10.1016/j.compind.2012.03.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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