Graphical Abstract Figure
Abstract
With the increasing popularity and deployment of unmanned surface vessels (USVs) all over the world, prognostics and health management (PHM) has become an indispensable tool for health monitoring, fault diagnosis, health prognosis, and maintenance of marine equipment on USVs. USVs are designed to undertake critical and extended missions, often in extreme conditions, without human intervention. This makes the USVs susceptible to equipment malfunction, which increases the probability of system failure during mission execution. In fact, in the absence of any crew onboard, system failure during a mission can create a great inconvenience for the concerned stakeholders, which compels them to design highly reliable USVs that must have integrated intelligent PHM systems onboard. To improve mission reliability and health management of USVs, researchers have been investigating and proposing PHM-based tools or frameworks that are claimed to operate in real time. This paper presents a comprehensive review of the existing literature on recent developments in PHM-related studies in the context of USVs. It covers a broad perspective of PHM on USVs, including system simulation, sensor data, data assimilation, data fusion, advancements in diagnosis and prognosis studies, and health management. After reviewing the literature, this study summarizes the lessons learned, identifies current gaps, and proposes a new system-level framework for developing a hybrid (offline–online) optimization-based PHM system for USVs in order to overcome some of the existing challenges.
References
1.
Zhang
, P.
, Gao
, Z.
, Cao
, L.
, Dong
, F.
, Zou
, Y.
, Wang
, K.
, Zhang
, Y.
, and Sun
, P.
, 2022
, “Marine Systems and Equipment Prognostics and Health Management: A Systematic Review From Health Condition Monitoring to Maintenance Strategy
,” Machines
, 10
(2
), p. 72
. 2.
Volponi
, A.
, Brotherton
, T.
, and Luppold
, R.
, 2004
, “Development of an Information Fusion System for Engine Diagnostics and Health Management
,” AIAA 1st Intelligent Systems Technical Conference
, Chicago, IL
, Sept. 20–22
, p. 6461
.3.
Ellefsen
, A. L.
, Cheng
, X.
, Holmeset
, F. T.
, Æsøy
, V.
, Zhang
, H.
, and Ushakov
, S.
, 2019
, “Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation
,” 2019 IEEE International Conference on Mechatronics and Automation (ICMA)
, Tianjin, China
, Aug. 4–7
, IEEE
, pp. 2195
–2200
.4.
Wang
, R.
, Chen
, H.
, and Guan
, C.
, 2021
, “A Bayesian Inference-Based Approach for Performance Prognostics Towards Uncertainty Quantification and Its Applications on the Marine Diesel Engine
,” ISA Trans.
, 118
, pp. 159
–173
. 5.
Helbing
, G.
, and Ritter
, M.
, 2018
, “Deep Learning for Fault Detection in Wind Turbines
,” Renew. Sustain. Energy Rev.
, 98
, pp. 189
–198
. 6.
Ruiz-Tagle Palazuelos
, A.
, Droguett
, E. L.
, and Pascual
, R.
, 2020
, “A Novel Deep Capsule Neural Network for Remaining Useful Life Estimation
,” Proc. Inst. Mech. Eng. Part O: J. Risk Reliab.
, 234
(1
), pp. 151
–167
.7.
Moradi
, R.
, and Groth
, K. M.
, 2020
, “Modernizing Risk Assessment: A Systematic Integration of PRA and PHM Techniques
,” Reliab. Eng. Syst. Saf.
, 204
, p. 107194
. 8.
Lewis
, A. D.
, and Groth
, K. M.
, 2020
, “A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems
,” Algorithms
, 13
(3
), p. 64
. 9.
Lewis
, A. D.
, and Groth
, K. M.
, 2023
, “A Comparison of DBN Model Performance in SIPPRA Health Monitoring Based on Different Data Stream Discretization Methods
,” Reliab. Eng. Syst. Saf.
, 236
, p. 109206
. 10.
Moradi
, R.
, Cofre-Martel
, S.
, Droguett
, E. L.
, Modarres
, M.
, and Groth
, K. M.
, 2022
, “Integration of Deep Learning and Bayesian Networks for Condition and Operation Risk Monitoring of Complex Engineering Systems
,” Reliab. Eng. Syst. Saf.
, 222
, p. 108433
. 11.
Guo
, Y.
, Wang
, H.
, Guo
, Y.
, Zhong
, M.
, Li
, Q.
, and Gao
, C.
, 2022
, “System Operational Reliability Evaluation Based on Dynamic Bayesian Network and XGBoost
,” Reliab. Eng. Syst. Saf.
, 225
, p. 108622
. 12.
Hazra
, I.
, Chatterjee
, A.
, Southgate
, J.
, Weiner
, M. J.
, Groth
, K. M.
, and Azarm
, S.
, 2023
, “A Reliability-Based Optimization Framework for Planning Operational Profiles for Unmanned Systems
,” Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 3A: 49th Design Automation Conference (DAC)
, Aug. 20–23
, Boston, MA
, p. V03AT03A047
.13.
Hazra
, I.
, Chatterjee
, A.
, Southgate
, J.
, Weiner
, M. J.
, Groth
, K. M.
, and Azarm
, S.
, 2024
, “A Reliability-Based Optimization Framework for Planning Operational Profiles for Unmanned Systems
,” J. Mech. Des.
, 146
(5
), p. 051704
. 14.
Ellefsen
, A. L.
, Æsøy
, V.
, Ushakov
, S.
, and Zhang
, H.
, 2019
, “A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships
,” IEEE Trans. Reliab.
, 68
(2
), pp. 720
–740
. 15.
Gao
, C.
, Guo
, Y.
, Zhong
, M.
, Liang
, X.
, Wang
, H.
, and Yi
, H.
, 2021
, “Reliability Analysis Based on Dynamic Bayesian Networks: A Case Study of an Unmanned Surface Vessel
,” Ocean Eng.
, 240
, p. 109970
. 16.
Yang
, R.
, Bremnes
, J. E.
, and Utne
, I. B.
, 2022
, “A System-Theoretic Approach to Hazard Identification of Operation With Multiple Autonomous Marine Systems (AMS)
,” The 32nd European Safety and Reliability Conference (ESREL 2022)
, Dublin, Ireland
, Aug. 28–Sept. 1
.17.
Yang
, R.
, and Utne
, I. B.
, 2022
, “Towards an Online Risk Model for Autonomous Marine Systems (AMS)
,” Ocean Eng.
, 251
, p. 111100
. 18.
Yan
, R.-J.
, Pang
, S.
, Sun
, H.-B.
, and Pang
, Y.-J.
, 2010
, “Development and Missions of Unmanned Surface Vehicle
,” J. Marine Sci. Appl.
, 9
, pp. 451
–457
. 19.
Manley
, J. E.
, 2008
, “Unmanned Surface Vehicles, 15 Years of Development
,” OCEANS 2008
, Quebec City, QC, Canada
, Sept. 15–18
, IEEE
, pp. 1
–4
.20.
O’Rourke
, R.
, 2023
, Navy Large Unmanned Surface and Undersea Vehicles: Background and Issues for Congress
, Congressional Research Service
.21.
Rao
, X.
, Sheng
, C.
, Guo
, Z.
, and Yuan
, C.
, 2022
, “A Review of Online Condition Monitoring and Maintenance Strategy for Cylinder Liner-Piston Rings of Diesel Engines
,” Mech. Syst. Signal Process.
, 165
, p. 108385
. 22.
Xie
, T.
, Wang
, T.
, He
, Q.
, Diallo
, D.
, and Claramunt
, C.
, 2020
, “A Review of Current Issues of Marine Current Turbine Blade Fault Detection
,” Ocean Eng.
, 218
, p. 108194
. 23.
Tsui
, K. L.
, Chen
, N.
, Zhou
, Q.
, Hai
, Y.
, and Wang
, W.
, 2015
, “Prognostics and Health Management: A Review on Data Driven Approaches
,” Math. Problems Eng.
, 2015
, p. 793161
. 24.
Zio
, E.
, 2022
, “Prognostics and Health Management (PHM): Where Are We and Where Do We (Need to) Go in Theory and Practice
,” Reliab. Eng. Syst. Saf.
, 218
, p. 108119
. 25.
Omri
, N.
, Al Masry
, Z.
, Mairot
, N.
, Giampiccolo
, S.
, and Zerhouni
, N.
, 2021
, “Towards an Adapted PHM Approach: Data Quality Requirements Methodology for Fault Detection Applications
,” Comput. Ind.
, 127
, p. 103414
. 26.
Belanger
, D.
, Furth
, M.
, Jansen
, K.
, and Reichard
, L.
, 2018
, “Toward the Use of Big Data in Smart Ships
,” 13th International Marine Design Conference, IMDC 2018
, Helsinki, Finland
, June 10–14
, CRC Press/Balkema
, pp. 897
–908
.27.
Weikun
, D.
, Nguyen
, K. T.
, Medjaher
, K.
, Christian
, G.
, and Morio
, J.
, 2023
, “Physics-Informed Machine Learning in Prognostics and Health Management: State of the Art and Challenges
,” Appl. Math. Model.
, 124
, pp. 325
–352
.28.
Yang
, R.
, Bremnes
, J. E.
, and Utne
, I. B.
, 2023
, “Online Risk Modeling of Autonomous Marine Systems: Case Study of Autonomous Operations Under Sea Ice
,” Ocean Eng.
, 281
, p. 114765
. 29.
Yang
, R.
, 2023
, “Methods and Models for Analyzing and Controlling the Safety in Operations of Autonomous Marine Systems
,” Ph.D. thesis, Norwegian University of Science and Technology
.30.
Yang
, R.
, Utne
, I. B.
, Liu
, Y.
, and Paltrinieri
, N.
, 2020
, “Dynamic Risk Analysis of Operation of the Autonomous Underwater Vehicle (AUV)
,” The 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management
, Venice, Italy
, Nov. 1–5
.31.
32.
Paris
, P.
, and Erdogan
, F.
, 1963
, “A Critical Analysis of Crack Propagation Laws
,” ASME J. Fluids Eng.
, 85
(4
), pp. 528
–533
.33.
Forman
, R.
, 1972
, “Study of Fatigue Crack Initiation From Flaws Using Fracture Mechanics Theory
,” Eng. Fract. Mech.
, 4
(2
), pp. 333
–345
. 34.
Zhang
, C.
, Cao
, C.
, Guo
, C.
, Li
, T.
, and Guo
, M.
, 2021
, “Navigation Multisensor Fault Diagnosis Approach for an Unmanned Surface Vessel Adopted Particle-Filter Method
,” IEEE Sensors J.
, 21
(23
), pp. 27093
–27105
. 35.
Zhou
, Y.
, Hu
, T.
, and Yang
, J.
, 2019
, “Design and Implementation of PHM System Framework for Unmanned Surface Vehicles
,” 2019 Prognostics and System Health Management Conference (PHM-Qingdao)
, Qingdao, China
, Oct. 25–27
, IEEE
, pp. 1
–5
.36.
Fossen
, T. I.
, 2011
, Handbook of Marine Craft Hydrodynamics and Motion Control
, John Wiley & Sons
.37.
Perez
, T.
, Smogeli
, O.
, Fossen
, T.
, and Sorensen
, A. J.
, 2006
, “An Overview of the Marine Systems Simulator (MSS): A Simulink Toolbox for Marine Control Systems
,” Model. Identif. Control
, 27
(4
), pp. 259
–275
. 38.
Wang
, W.
, Zhang
, H.
, Li
, Y.
, Zhang
, Z.
, Luo
, X.
, and Xie
, S.
, 2022
, “USVs-Sim: A General Simulation Platform for Unmanned Surface Vessels Autonomous Learning
,” Concurrency Comput.: Pract. Exp.
, 34
(3
), p. e6567
. 39.
Wang
, N.
, Gao
, Y.
, and Zhang
, X.
, 2021
, “Data-Driven Performance-Prescribed Reinforcement Learning Control of an Unmanned Surface Vehicle
,” IEEE Trans. Neural Netw. Learn. Syst.
, 32
(12
), pp. 5456
–5467
. 40.
Bingham
, B. S.
, Prechtl
, E. F.
, and Wilson
, R. A.
, 2009
, “Design Requirements for Autonomous Multivehicle Surface-Underwater Operations
,” Marine Technol. Soc. J.
, 43
(2
), pp. 61
–72
. 41.
Schmitt
, K.
, 2010
, “Modeling and Simulation of an All Electric Ship in Random Seas
,” Ph.D. thesis, Massachusetts Institute of Technology
.42.
Bickford
, J.
, Van Bossuyt
, D. L.
, Beery
, P.
, and Pollman
, A.
, 2020
, “Operationalizing Digital Twins Through Model-Based Systems Engineering Methods
,” Syst. Eng.
, 23
(6
), pp. 724
–750
. 43.
Utne
, I. B.
, Rokseth
, B.
, Sørensen
, A. J.
, and Vinnem
, J. E.
, 2020
, “Towards Supervisory Risk Control of Autonomous Ships
,” Reliab. Eng. Syst. Saf.
, 196
, p. 106757
. 44.
Yang
, R.
, Vatn
, J.
, and Utne
, I. B.
, 2023
, “Dynamic Maintenance Planning for Autonomous Marine Systems (AMS) and Operations
,” Ocean Eng.
, 278
, p. 114492
. 45.
Elkins
, L.
, Sellers
, D.
, and Monach
, W. R.
, 2010
, “The Autonomous Maritime Navigation (AMN) Project: Field Tests, Autonomous and Cooperative Behaviors, Data Fusion, Sensors, and Vehicles
,” J. Field Rob.
, 27
(6
), pp. 790
–818
. 46.
Khaleghi
, B.
, Khamis
, A.
, Karray
, F. O.
, and Razavi
, S. N.
, 2013
, “Multisensor Data Fusion: A Review of the State-of-the-Art
,” Inf. Fusion
, 14
(1
), pp. 28
–44
. 47.
Gribbestad
, M.
, Hassan
, M. U.
, and Hameed
, I. A.
, 2021
, “Transfer Learning for Prognostics and Health Management (PHM) of Marine Air Compressors
,” J. Marine Sci. Eng.
, 9
(1
), p. 47
. 48.
Kim
, J.-Y.
, Lee
, T.-H.
, Lee
, S.-H.
, Lee
, J.-J.
, Lee
, W.-K.
, Kim
, Y.-J.
, and Park
, J.-W.
, 2022
, “A Study on Deep Learning-Based Fault Diagnosis and Classification for Marine Engine System Auxiliary Equipment
,” Processes
, 10
(7
), p. 1345
. 49.
Jeong
, S.-K.
, Ji
, D.-H.
, Oh
, M.
, Park
, H.
, Baeg
, S.
, and Lee
, J.
, 2023
, “A Study on Anomaly Detection of Unmanned Marine Systems Using Machine Learning
,” Meas. Control
, 56
(3–4
), pp. 470
–480
. 50.
Singh
, R.
, and Bhushan
, B.
, 2019
, “Fault Classification for Unmanned Surface Vehicles Using Supervised Learning Methods
,” 2019 International Conference on Power Electronics, Control and Automation (ICPECA)
, New Delhi, India
, Nov. 16–17
, IEEE
, pp. 1
–6
.51.
Stoumpos
, S.
, and Theotokatos
, G.
, 2022
, “A Novel Methodology for Marine Dual Fuel Engines Sensors Diagnostics and Health Management
,” Int. J. Engine Res.
, 23
(6
), pp. 974
–994
. 52.
Ellefsen
, A. L.
, Han
, P.
, Cheng
, X.
, Holmeset
, F. T.
, Æsøy
, V.
, and Zhang
, H.
, 2020
, “Online Fault Detection in Autonomous Ferries: Using Fault-Type Independent Spectral Anomaly Detection
,” IEEE Trans. Instrum. Meas.
, 69
(10
), pp. 8216
–8225
.53.
Tsai
, C.-M.
, Wang
, C.-S.
, Chung
, Y.-J.
, Sun
, Y.-D.
, and Perng
, J.-W.
, 2021
, “Multi-sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning
,” Sensors
, 21
(21
), p. 7187
. 54.
Kalgren
, P. W.
, Byington
, C. S.
, Roemer
, M. J.
, and Watson
, M. J.
, 2006
, “Defining PHM, a Lexical Evolution of Maintenance and Logistics
,” 2006 IEEE Autotestcon
, Anaheim, CA
, Sept. 18–21
, IEEE
, pp. 353
–358
.55.
Chiu
, S.
, Provan
, G.
, Chen
, Y.-L.
, Maturana
, F.
, Balasubramanian
, S.
, Staron
, R.
, and Vasko
, D.
, 2001
, “Shipboard System Diagnostics and Reconfiguration Using Model-Based Autonomous Cooperative Agents
,” IFAC Proc. Vol.
, 34
(7
), pp. 323
–329
. 56.
Zhu
, K.
, and Wang
, Y.
, 2022
, “Event-Triggered Sensor Fault Estimation of Unreliable Networked Unmanned Surface Vehicle System With Correlated Noises
,” IEEE Trans. Veh. Technol.
, 71
(3
), pp. 2527
–2537
. 57.
Ismail
, M. A.
, Balaban
, E.
, and Windelberg
, J.
, 2022
, “Spall Fault Quantification Method for Flight Control Electromechanical Actuator
,” Actuators
, 11
(2
), p. 29
. 58.
Miller
, R. H.
, and Larsen
, M. L.
, 2003
, “Optimal Fault Detection and Isolation Filters for Flight Vehicle Performance Monitoring
,” 2003 IEEE Aerospace Conference Proceedings (Cat. No. 03TH8652)
, Big Sky, MT
, Mar. 8–15
, Vol. 7, IEEE
, pp. 3197
–3203
.59.
Speck
, A.
, Croux
, A.
, Jarrot
, A.
, Strunk
, G.
, Choi
, G.
, Osedach
, T. P.
, Gelman
, A.
, et al., 2020
, “Supervised Autonomy for Advanced Perception and Hydrocarbon Leak Detection
,” Global Oceans 2020: Singapore–US Gulf Coast
, Biloxi, MS
, Oct. 5–30
, IEEE
, pp. 1
–6
.60.
Breivik
, A.
, 2021
, “Fault Detection and Diagnosis of Induction Motor for Ship Propulsion by Utilizing Electrical Signature and Finite Element Method
,” Master’s thesis, NTNU
.61.
Wen-Hao
, W.
, Guo-bing
, C.
, and Zi-chun
, Y.
, 2021
, “The Application and Challenge of Digital Twin Technology in Ship Equipment
,” J. Phys.: Conf. Ser.
, 1939
, p. 012068
.62.
Wang
, M.
, Qin
, G.
, Chen
, J.
, and Liao
, Y.
, 2020
, “Design of Vibration Monitoring and Fault Diagnosis System for Marine Diesel Engine
,” 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan)
, Jinan, China
, Oct. 23–25
, IEEE
, pp. 1
–4
.63.
Poderico
, M.
, Morani
, G.
, and Corraro
, F.
, 2014
, “Fault Detection Isolation and Reconfiguration Algorithms for Atmospheric Re-entry
,” 22nd Mediterranean Conference on Control and Automation
, Palermo, Italy
, June 16–19
, IEEE
, pp. 1273
–1280
.64.
Zhou
, Z.
, Zhong
, M.
, and Wang
, Y.
, 2019
, “Fault Diagnosis Observer and Fault-Tolerant Control Design for Unmanned Surface Vehicles in Network Environments
,” IEEE Access
, 7
, p. 173694
. 65.
Rotondo
, D.
, Cristofaro
, A.
, Johansen
, T. A.
, Nejjari
, F.
, and Puig
, V.
, 2015
, “Icing Detection in Unmanned Aerial Vehicles With Longitudinal Motion Using an LPV Unknown Input Observer
,” 2015 IEEE Conference on Control Applications (CCA)
, Sydney, Australia
, Sept. 21–23
, IEEE
, pp. 984
–989
.66.
Cristofaro
, A.
, and Johansen
, T. A.
, 2015
, “An Unknown Input Observer Approach to Icing Detection for Unmanned Aerial Vehicles With Linearized Longitudinal Motion
,” 2015 American Control Conference (ACC)
, Chicago, IL
, July 1–3
, IEEE
, pp. 207
–213
.67.
Ko
, N. Y.
, Song
, G.
, Choi
, H. T.
, and Sur
, J.
, 2021
, “Fault Detection and Diagnosis of Sensors and Actuators for Unmanned Surface Vehicles
,” 2021 21st International Conference on Control, Automation and Systems (ICCAS)
, Jeju, South Korea
, Oct. 12–15
, IEEE
, pp. 1451
–1453
.68.
Bodden
, D. S.
, Hadden
, W.
, Grube
, B. E.
, and Clements
, N. S.
, 2005
, “PHM as a Design Variable in Air Vehicle Conceptual Design
,” 2005 IEEE Aerospace Conference
, Big Sky, MT
, Mar. 5–12
, IEEE
, pp. 1
–11
.69.
Bodden
, D. S.
, Hadden
, W.
, Grube
, B. E.
, and Clements
, N. S.
, 2006
, “Prognostics and Health Management as Design Variable in Air-Vehicle Conceptual Design
,” J. Aircraft
, 43
(4
), pp. 1053
–1058
. 70.
Kladis
, G.
, Economou
, J.
, Tsourdos
, A.
, White
, B.
, and Knowles
, K.
, 2009
, “Fault Diagnosis With Matrix Analysis for Electrically Actuated Unmanned Aerial Vehicles
,” Proc. Inst. Mech. Eng. Part G: J. Aerosp. Eng.
, 223
(5
), pp. 543
–563
. 71.
Ruiqian
, L.
, Juan
, X.
, and Hongfu
, Z.
, 2020
, “Automated Surface Defects Acquisition System of Civil Aircraft Based on Unmanned Aerial Vehicles
,” 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)
, Weihai, China
, Oct. 14–16
, IEEE
, pp. 729
–733
.72.
Bateman
, F.
, Noura
, H.
, and Ouladsine
, M.
, 2007
, “Actuators Fault Diagnosis and Tolerant Control for an Unmanned Aerial Vehicle
,” 2007 IEEE International Conference on Control Applications
, Singapore
, Oct. 1–3
, IEEE
, pp. 1061
–1066
.73.
Nguyen
, D. C.
, Ding
, M.
, Pathirana
, P. N.
, Seneviratne
, A.
, Li
, J.
, Niyato
, D.
, Dobre
, O.
, and Poor
, H. V.
, 2021
, “6G Internet of Things: A Comprehensive Survey
,” IEEE Internet Things J.
, 9
(1
), pp. 359
–383
. 74.
Alabe
, L. W.
, Kea
, K.
, Han
, Y.
, Min
, Y. J.
, and Kim
, T.
, 2022
, “A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System
,” Sensors
, 22
(22
), p. 8981
. 75.
Ma
, Y.
, Guo
, Z.
, Su
, J.
, Chen
, Y.
, Du
, X.
, Yang
, Y.
, Li
, C.
, Lin
, Y.
, and Geng
, Y.
, 2014
, “Deep Learning for Fault Diagnosis Based on Multi-sourced Heterogeneous Data
,” 2014 International Conference on Power System Technology
, Chengdu, China
, Oct. 20–22
, IEEE
, pp. 740
–745
.76.
Xia
, M.
, Li
, T.
, Liu
, L.
, Xu
, L.
, and de Silva
, C. W.
, 2017
, “Intelligent Fault Diagnosis Approach With Unsupervised Feature Learning by Stacked Denoising Autoencoder
,” IET Sci. Meas. Technol.
, 11
(6
), pp. 687
–695
.77.
Barker
, J.
, Bhowmik
, N.
, and Breckon
, T.
, 2021
, “Semi-supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery
,” preprint arXiv:2112.00556.78.
Jing
, L.
, Zhao
, M.
, Li
, P.
, and Xu
, X.
, 2017
, “A Convolutional Neural Network Based Feature Learning and Fault Diagnosis Method for the Condition Monitoring of Gearbox
,” Measurement
, 111
, pp. 1
–10
. 79.
Jia
, F.
, Lei
, Y.
, Lin
, J.
, Zhou
, X.
, and Lu
, N.
, 2016
, “Deep Neural Networks: A Promising Tool for Fault Characteristic Mining and Intelligent Diagnosis of Rotating Machinery With Massive Data
,” Mech. Syst. Signal Process.
, 72
, pp. 303
–315
. 80.
Djenouri
, Y.
, Belhadi
, A.
, Djenouri
, D.
, Srivastava
, G.
, and Lin
, J. C.-W.
, 2022
, “Intelligent Deep Fusion Network for Anomaly Identification in Maritime Transportation Systems
,” IEEE Trans. Intell. Transp. Syst.
, 24
(2
), pp. 2392
–2400
.81.
Lee
, S.
, Lee
, T.
, Kim
, J.
, Lee
, J.
, Ryu
, K.
, Kim
, Y.
, and Park
, J.-W.
, 2022
, “A Study on the Application of Discrete Wavelet Decomposition for Fault Diagnosis on a Ship Oil Purifier
,” Processes
, 10
(8
), p. 1468
. 82.
Tan
, J.
, Fan
, Y.
, Yan
, P.
, Wang
, C.
, and Feng
, H.
, 2019
, “Sliding Mode Fault Tolerant Control for Unmanned Aerial Vehicle With Sensor and Actuator Faults
,” Sensors
, 19
(3
), p. 643
. 83.
Yu
, Z.
, Zhang
, Y.
, Jiang
, B.
, Su
, C.-Y.
, Fu
, J.
, Jin
, Y.
, and Chai
, T.
, 2021
, “Fractional Order PID-Based Adaptive Fault-Tolerant Cooperative Control of Networked Unmanned Aerial Vehicles Against Actuator Faults and Wind Effects With Hardware-in-the-Loop Experimental Validation
,” Control Eng. Pract.
, 114
, p. 104861
. 84.
Han
, X.
, Wang
, Z.
, Xie
, M.
, He
, Y.
, Li
, Y.
, and Wang
, W.
, 2021
, “Remaining Useful Life Prediction and Predictive Maintenance Strategies for Multi-state Manufacturing Systems Considering Functional Dependence
,” Reliab. Eng. Syst. Saf.
, 210
, p. 107560
. 85.
de Pater
, I.
, and Mitici
, M.
, 2023
, “Developing Health Indicators and RUL Prognostics for Systems With Few Failure Instances and Varying Operating Conditions Using a LSTM Autoencoder
,” Eng. Appl. Artif. Intell.
, 117
, p. 105582
. 86.
Song
, C.
, Liu
, K.
, and Zhang
, X.
, 2017
, “Integration of Data-Level Fusion Model and Kernel Methods for Degradation Modeling and Prognostic Analysis
,” IEEE Trans. Reliab.
, 67
(2
), pp. 640
–650
. 87.
Loukopoulos
, P.
, Zolkiewski
, G.
, Bennett
, I.
, Sampath
, S.
, Pilidis
, P.
, Li
, X.
, and Mba
, D.
, 2019
, “Abrupt Fault Remaining Useful Life Estimation Using Measurements From a Reciprocating Compressor Valve Failure
,” Mech. Syst. Signal Process.
, 121
, pp. 359
–372
. 88.
Liu
, K.
, Chehade
, A.
, and Song
, C.
, 2015
, “Optimize the Signal Quality of the Composite Health Index Via Data Fusion for Degradation Modeling and Prognostic Analysis
,” IEEE Trans. Autom. Sci. Eng.
, 14
(3
), pp. 1504
–1514
. 89.
Guo
, C.
, and Utne
, I. B.
, 2022
, “Development of Risk Indicators for Losing Navigational Control of Autonomous Ships
,” Ocean Eng.
, 266
, p. 113204
. 90.
Hanachi
, H.
, Liu
, J.
, Banerjee
, A.
, Chen
, Y.
, and Koul
, A.
, 2014
, “A Physics-Based Modeling Approach for Performance Monitoring in Gas Turbine Engines
,” IEEE Trans. Reliab.
, 64
(1
), pp. 197
–205
. 91.
Baraldi
, P.
, Bonfanti
, G.
, and Zio
, E.
, 2018
, “Differential Evolution-Based Multi-objective Optimization for the Definition of a Health Indicator for Fault Diagnostics and Prognostics
,” Mech. Syst. Signal Process.
, 102
, pp. 382
–400
. 92.
Pan
, H.
, Lü
, Z.
, Wang
, H.
, Wei
, H.
, and Chen
, L.
, 2018
, “Novel Battery State-of-Health Online Estimation Method Using Multiple Health Indicators and an Extreme Learning Machine
,” Energy
, 160
, pp. 466
–477
. 93.
Rebello
, S.
, Yu
, H.
, and Ma
, L.
, 2018
, “An Integrated Approach for System Functional Reliability Assessment Using Dynamic Bayesian Network and Hidden Markov Model
,” Reliab. Eng. Syst. Saf.
, 180
, pp. 124
–135
. 94.
Ahmadzadeh
, F.
, and Lundberg
, J.
, 2014
, “Remaining Useful Life Estimation: Review
,” Int. J. Syst. Assurance Eng. Manage.
, 5
, pp. 461
–474
. 95.
Tang
, W.
, Roman
, D.
, Dickie
, R.
, Robu
, V.
, and Flynn
, D.
, 2020
, “Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems
,” Energies
, 13
(18
), p. 4676
. 96.
Abbas
, M.
, and Shafiee
, M.
, 2020
, “An Overview of Maintenance Management Strategies for Corroded Steel Structures in Extreme Marine Environments
,” Marine Struct.
, 71
, p. 102718
. 97.
Nguyen
, V.
, Seshadrinath
, J.
, Wang
, D.
, Nadarajan
, S.
, and Vaiyapuri
, V.
, 2017
, “Model-Based Diagnosis and RUL Estimation of Induction Machines Under Interturn Fault
,” IEEE Trans. Ind. Appl.
, 53
(3
), pp. 2690
–2701
. 98.
Bazilevs
, Y.
, Marsden
, A. L.
, di Scalea
, F. L.
, Majumdar
, A.
, and Tatineni
, M.
, 2012
, “Toward a Computational Steering Framework for Large-Scale Composite Structures Based on Continually and Dynamically Injected Sensor Data
,” Procedia Comput. Sci.
, 9
, pp. 1149
–1158
. 99.
Zhai
, Q.
, and Ye
, Z.-S.
, 2017
, “RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model
,” IEEE Trans. Ind. Inf.
, 13
(6
), pp. 2911
–2921
. 100.
Tang
, D.
, Cao
, J.
, and Yu
, J.
, 2019
, “Remaining Useful Life Prediction for Engineering Systems Under Dynamic Operational Conditions: A Semi-Markov Decision Process-Based Approach
,” Chin. J. Aeronaut.
, 32
, pp. 627
–638
. 101.
Du
, Y.
, Duan
, C.
, and Wu
, T.
, 2022
, “Lubricating Oil Deterioration Modeling and Remaining Useful Life Prediction Based on Hidden Semi-Markov Modeling
,” Proc. Inst. Mech. Eng. Part J: J. Eng. Tribol.
, 236
(5
), pp. 916
–923
. 102.
Zhang
, Y.
, Yang
, Y.
, Li
, H.
, Xiu
, X.
, and Liu
, W.
, 2021
, “A Data-Driven Modeling Method for Stochastic Nonlinear Degradation Process With Application to RUL Estimation
,” IEEE Trans. Syst. Man Cybern.: Syst.
, 52
(6
), pp. 3847
–3858
. 103.
BahooToroody
, A.
, Abaei
, M. M.
, Banda
, O. V.
, Montewka
, J.
, and Kujala
, P.
, 2022
, “On Reliability Assessment of Ship Machinery System in Different Autonomy Degree; A Bayesian-Based Approach
,” Ocean Eng.
, 254
, p. 111252
. 104.
Zhao
, S.
, Zhang
, Y.
, Wang
, S.
, Zhou
, B.
, and Cheng
, C.
, 2019
, “A Recurrent Neural Network Approach for Remaining Useful Life Prediction Utilizing a Novel Trend Features Construction Method
,” Measurement
, 146
, pp. 279
–288
. 105.
Zhang
, L.
, Wang
, B.
, Yuan
, X.
, and Liang
, P.
, 2022
, “Remaining Useful Life Prediction Via Improved CNN, GRU and Residual Attention Mechanism With Soft Thresholding
,” IEEE Sensors J.
, 22
(15
), pp. 15178
–15190
. 106.
Mitici
, M.
, de Pater
, I.
, Barros
, A.
, and Zeng
, Z.
, 2023
, “Dynamic Predictive Maintenance for Multiple Components Using Data-Driven Probabilistic RUL Prognostics: The Case of Turbofan Engines
,” Reliab. Eng. Syst. Saf.
, 234
, p. 109199
. 107.
Han
, P.
, Ellefsen
, A. L.
, Li
, G.
, Æsøy
, V.
, and Zhang
, H.
, 2021
, “Fault Prognostics Using LSTM Networks: Application to Marine Diesel Engine
,” IEEE Sensors J.
, 21
(22
), pp. 25986
–25994
. 108.
Yang
, K.
, Wang
, Y.-J.
, Yao
, Y.-N.
, and Fan
, S.-D.
, 2021
, “Remaining Useful Life Prediction Via Long-Short Time Memory Neural Network With Novel Partial Least Squares and Genetic Algorithm
,” Qual. Reliab. Eng. Int.
, 37
(3
), pp. 1080
–1098
. 109.
Bae
, J.
, and Xi
, Z.
, 2022
, “Learning of Physical Health Timestep Using the LSTM Network for Remaining Useful Life Estimation
,” Reliab. Eng. Syst. Saf.
, 226
, p. 108717
. 110.
Nguyen
, K. T.
, Medjaher
, K.
, and Gogu
, C.
, 2022
, “Probabilistic Deep Learning Methodology for Uncertainty Quantification of Remaining Useful Lifetime of Multi-component Systems
,” Reliab. Eng. Syst. Saf.
, 222
, p. 108383
. 111.
BahooToroody
, A.
, Abaei
, M. M.
, Banda
, O. V.
, Kujala
, P.
, De Carlo
, F.
, and Abbassi
, R.
, 2022
, “Prognostic Health Management of Repairable Ship Systems Through Different Autonomy Degree; From Current Condition to Fully Autonomous Ship
,” Reliab. Eng. Syst. Saf.
, 221
, p. 108355
. 112.
Abaei
, M. M.
, Hekkenberg
, R.
, BahooToroody
, A.
, Banda
, O. V.
, and van Gelder
, P.
, 2022
, “A Probabilistic Model to Evaluate the Resilience of Unattended Machinery Plants in Autonomous Ships
,” Reliab. Eng. Syst. Saf.
, 219
, p. 108176
. 113.
Ellefsen
, A. L.
, Bjørlykhaug
, E.
, Æsøy
, V.
, Ushakov
, S.
, and Zhang
, H.
, 2019
, “Remaining Useful Life Predictions for Turbofan Engine Degradation Using Semi-supervised Deep Architecture
,” Reliab. Eng. Syst. Saf.
, 183
, pp. 240
–251
.114.
Cai
, B.
, Shao
, X.
, Liu
, Y.
, Kong
, X.
, Wang
, H.
, Xu
, H.
, and Ge
, W.
, 2019
, “Remaining Useful Life Estimation of Structure Systems Under the Influence of Multiple Causes: Subsea Pipelines as a Case Study
,” IEEE Trans. Ind. Electron.
, 67
(7
), pp. 5737
–5747
. 115.
Nielsen
, J. S.
, and Sørensen
, J. D.
, 2017
, “Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades
,” Energies
, 10
(5
), p. 664
. 116.
Khorasgani
, H.
, Biswas
, G.
, and Sankararaman
, S.
, 2016
, “Methodologies for System-Level Remaining Useful Life Prediction
,” Reliab. Eng. Syst. Saf.
, 154
, pp. 8
–18
. 117.
Zhicai
, Z.
, Dongfeng
, L.
, and Xinfa
, S.
, 2014
, “Research on Combination of Data-Driven and Probability-Based Prognostics Techniques for Equipments
,” 2014 Prognostics and System Health Management Conference (PHM-2014 Hunan)
, Zhangjiajie, China
, Aug. 24–27
, IEEE
, pp. 323
–326
.118.
Hoyland
, A.
, and Rausand
, M.
, 2009
, System Reliability Theory: Models and Statistical Methods
, John Wiley & Sons
.119.
Zhang
, L.
, Ji
, Z.
, Wang
, X.
, Yang
, R.
, Liu
, L.
, and Jin
, J.
, 2019
, “Design of the Power Supply System and the PHM Architecture for Unmanned Surface Vehicle
,” 2019 Prognostics and System Health Management Conference (PHM-Qingdao)
, Qingdao, China
, Oct. 25–27
, IEEE
, pp. 1
–6
.120.
Al Hage
, J.
, El Najjar
, M. E.
, and Pomorski
, D.
, 2017
, “Multi-sensor Fusion Approach With Fault Detection and Exclusion Based on the Kullback–Leibler Divergence: Application on Collaborative Multi-robot System
,” Inf. Fusion
, 37
, pp. 61
–76
. 121.
Wan
, L.
, Liu
, R.
, Sun
, L.
, Nie
, H.
, and Wang
, X.
, 2022
, “UAV Swarm Based Radar Signal Sorting Via Multi-source Data Fusion: A Deep Transfer Learning Framework
,” Inf. Fusion
, 78
, pp. 90
–101
. 122.
Lee
, E. B. K.
, Van Bossuyt
, D. L.
, and Bickford
, J. F.
, 2021
, “Digital Twin-Enabled Decision Support in Mission Engineering and Route Planning
,” Systems
, 9
(4
), p. 82
. 123.
Fenton
, N.
, and Neil
, M.
, 2018
, Risk Assessment and Decision Analysis With Bayesian Networks
, CRC Press
.124.
Silva
, G. S. M.
, Parhizkar
, T.
, and Droguett
, E. L.
, 2022
, “Quantum Fault Trees
,” preprint arXiv:2204.10877.125.
Ajagekar
, A.
, and You
, F.
, 2021
, “Quantum Computing Based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems
,” Appl. Energy
, 303
, p. 117628
. 
