[go: up one dir, main page]

Kalafatelis et al., 2024 - Google Patents

A survey on predictive maintenance in the maritime industry using machine and federated learning

Kalafatelis et al., 2024

View PDF
Document ID
9741595606701646211
Author
Kalafatelis A
Nomikos N
Giannopoulos A
Trakadas P
Publication year
Publication venue
Authorea Preprints

External Links

Snippet

The maritime industry is responsible for the transportation of over 80% of worldwide commodities. Currently, the industry heavily relies on traditional maintenance strategies, often leading to excessive and preventable downtime events, failing to ensure operational …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
Makridis et al. Predictive maintenance leveraging machine learning for time-series forecasting in the maritime industry
Stadtmann et al. Digital twins in wind energy: Emerging technologies and industry-informed future directions
Gkerekos et al. A novel, data-driven heuristic framework for vessel weather routing
De Silva et al. Toward intelligent industrial informatics: A review of current developments and future directions of artificial intelligence in industrial applications
US11544930B2 (en) Systems and methods for modeling and controlling physical dynamical systems using artificial intelligence
Angelopoulos et al. Federated learning-aided prognostics in the shipping 4.0: Principles, workflow, and use cases
Han et al. A deep learning approach to detect and isolate thruster failures for dynamically positioned vessels using motion data
Kalafatelis et al. A survey on predictive maintenance in the maritime industry using machine and federated learning
Velasco-Gallego et al. Development of a time series imaging approach for fault classification of marine systems
Vorkapić et al. Machine learning methods in monitoring operating behaviour of marine two-stroke diesel engine
Bui et al. A comprehensive review on big data-based potential applications in marine shipping management
Medina-Oliva et al. Prognostics assessment using fleet-wide ontology
Velasco-Gallego et al. Mar-RUL: A remaining useful life prediction approach for fault prognostics of marine machinery
Zhu et al. A survey on intelligent predictive maintenance (IPdM) in the era of fully connected intelligence
Asimakopoulos et al. Data-driven condition monitoring of two-stroke marine diesel engine piston rings with machine learning
Hua et al. A Literature Survey on the role of Artificial intelligence in conditioning monitoring
Liang et al. A Digital Twin Model Construction Method for Ships
Wei et al. ROSIS: Resilience oriented security inspection system against false data injection attacks
Xue et al. Vessel track prediction based on fractional gradient recurrent neural network with maneuvering behavior identification
Li et al. Multi-level attention graph feature fusion smooth prognostics approach for aircraft engines remaining useful life prediction
Zeng et al. The marine diesel engine exhaust gas temperature baseline model based on particle swarm optimised generalised regression neural network
Mikulić et al. A review of artificial intelligence applications in ship structures
Rahadi et al. The necessity of implementing AI for enhancing safety in the Indonesian passenger shipping fleet
O'Donncha et al. Towards intelligent ship-edge computing enabling automated configuration of ship models and adaptive self-learning
Ramachadran et al. Review of Contemporary Methods for Reliability Analysis in Aircraft Components