Asset Condition, Information Systems and Decision Models [electronic resource] /
Amadi-Echendu, Joe E.editor.
Brown, Kerry.editor.
Willett, Roger.editor.
Mathew, Joseph.editor.
Asset Condition, Information Systems and Decision Models, is the second volume of the Engineering Asset Management Review Series. The manuscripts provide examples of implementations of asset information systems as well as some practical applications of condition data for diagnostics and prognostics. The increasing trend is towards prognostics rather than diagnostics, hence the need for assessment and decision models that promote the conversion of condition data into prognostic information to improve life-cycle planning for engineered assets. The research papers included here serve to support the on-going development of Condition Monitoring standards. This volume comprises selected papers from the 1st, 2nd, and 3rd World Congresses on Engineering Asset Management, which were convened under the auspices of ISEAM in collaboration with a number of organisations, including CIEAM Australia, Asset Management Council Australia, BINDT UK, and Chinese Academy of Sciences, Beijing University of Chemical Technology, China. Asset Condition, Information Systems and Decision Models will be of particular interest to finance, maintenance, and operations personnel whose roles directly affect the capability value of engineering asset base, as well as asset managers in both industry and government.
Description based upon print version of record.
Includes bibliographical references.
Improving Asset Management Process Modelling and Integration -- Field-wide Integrated Planning In Complex and Remote Operational Environments: Reflections based on an industrial case study -- Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis -- The Concept of the Distributed Diagnostic System for Structural Health Monitoring of Critical Elements of Infrastructure Objects -- Vibration Based Wear Assessment in Slurry Pumps -- Machine Prognostics Based on Health State Estimation Using SVM -- Information System Implementation for Asset Management: A theoretical perspective -- A Flexible Asset Maintenance Decision-Making Process Model -- Optimising Preventive Maintenance Strategy for Production Lines -- Approaches to Information Quality Management: State of the Practice of UK Asset Intensive Organizations -- Modelling Risk in Discrete Multi-State Risk in Discrete Multi-State Repairable Systems -- Managing the Risks of Adverse Operational Requirements in Power Generation: Case Study in Gas and Hydro Turbines.
Asset Condition, Information Systems and Decision Models, is the second volume of the Engineering Asset Management Review Series. The manuscripts provide examples of implementations of asset information systems as well as some practical applications of condition data for diagnostics and prognostics. The increasing trend is towards prognostics rather than diagnostics, hence the need for assessment and decision models that promote the conversion of condition data into prognostic information to improve life-cycle planning for engineered assets. The research papers included here serve to support the on-going development of Condition Monitoring standards. This volume comprises selected papers from the 1st, 2nd, and 3rd World Congresses on Engineering Asset Management, which were convened under the auspices of ISEAM in collaboration with a number of organisations, including CIEAM Australia, Asset Management Council Australia, BINDT UK, and Chinese Academy of Sciences, Beijing University of Chemical Technology, China. Asset Condition, Information Systems and Decision Models will be of particular interest to finance, maintenance, and operations personnel whose roles directly affect the capability value of engineering asset base, as well as asset managers in both industry and government.
Engineering economics.
Engineering economy.
Management.
Industrial management.
Finance.
Quality control.
Reliability.
Industrial safety.
Information technology.
Business-Data processing.
Engineering Economics, Organization, Logistics, Marketing.
Innovation/Technology Management.
Finance, general.
Quality Control, Reliability, Safety and Risk.
IT in Business.
Engineering Asset Management Review