[go: up one dir, main page]

Pereira et al., 2016 - Google Patents

Risk assessment using bayesian belief networks and analytic hierarchy process applicable to jet engine high pressure turbine assembly

Pereira et al., 2016

Document ID
12268200521149614532
Author
Pereira J
Fragoso M
Todorov M
Publication year
Publication venue
IFAC-PapersOnLine

External Links

Snippet

In the jet engines manufacturing/overhauling process, risk analysis is essential to prevent engine failure. The selection and use of an adequate analysis method to ensure software, hardware and operation reliability is very important. This paper proposes a framework for …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • 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
    • G06Q10/0635—Risk analysis
    • 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
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by quality surveillance of production
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • 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
    • G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
    • 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
    • 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
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring

Similar Documents

Publication Publication Date Title
Pereira et al. Risk assessment using bayesian belief networks and analytic hierarchy process applicable to jet engine high pressure turbine assembly
Hu et al. Engineering design under uncertainty and health prognostics
Jin Reliability engineering and services
Chen et al. An approach for optimal measurements selection on gas turbine engine fault diagnosis
Boring How many performance shaping factors are necessary for human reliability analysis?
Durmanov et al. Simulation modeling, analysis and performance assessment
Lidell et al. The current and future challenges for virtual commissioning and digital twins of production lines
Patel et al. Evaluating the use of artificial neural networks and graph complexity to predict automotive assembly quality defects
Kumar et al. Predictive analytics in system reliability
Ma et al. Joint optimization of production and maintenance using Monte Carlo method and metaheuristic algorithms
Zhang et al. Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back‐Propagation Neural Network
Sgarbossa et al. Using systemability function for periodic replacement policy in real environments
Roy et al. Building a rigorous foundation for performance assurance assessment techniques for “smart” manufacturing systems
Vaisi et al. Schedule‐Allocate and Robust Sequencing in Three‐Machine Robotic Cell under Breakdowns
Groth A framework for using SACADA to enhance the qualitative and quantitative basis of HRA
Pereira Hidden Value in Maintenance System Data: Using Machine Learning to Correlate and Predict the Risk of Asset Failures
Wang et al. Diagnosis Index System Setup for Implementation Status Management in Large‐Scale Construction Projects
Kurtel et al. Cohesive software measurement planning framework using ISO standards: a case study from logistics service sector
Suri et al. Key inspection characteristics
Sakhaei Development of an operational procedure based on issues management methodologies in the FRACAS process
Okabe et al. A method for determining failure modes associated with damage/fracture of materials in early design stages
Austin et al. Using Bayesian networks to validate technology readiness assessments of systems
Rahmani et al. A comprehensive assessment model for requirement-based test case prioritization: Integrating internal factors and dependency analysis through expert evaluation
Khedry et al. Proposing a New Dynamic Maintenance Model for Reliability Improvement By Antifragility Approach: A Case Study in Iranian Gas Transmission Company-Zone10
Pereira et al. Risk assessment of failure in the assembly of rotors during manufacturing process-a case study