ASSET HEALTH PROGNOSIS INCORPORATING RELIABILITY DATA AND CONDITION MONITORING HISTORIES
This work demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset reliability. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately and further ahead than similar models that neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.
Condition-based Maintenance Condition Monitoring Prognostics Reliability Suspended Data Artificial Neural Networks
Aiwina Heng Andy Tan Joseph Mathew
CRC for Integrated Engineering Asset Management, Faculty of Built Environment & Engineering, Queensland University of Technology, Brisbane, QLD 4001, Australia
国际会议
北京
英文
666-672
2008-10-27（万方平台首次上网日期，不代表论文的发表时间）