An Artificial Neural Network Approach for Remaining Useful Life Prediction of Equipments Subject to Condition Monitoring

摘要：

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost.An artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring.The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output Techniques are introduced to reduce the effects of the noise factors that are irrelevant to equipment degradation.The proposed method is validated using real-world vibration monitoring data.

关键词： Remaining useful life prediction artificial neural network accurate bearing

作者: Zhigang Tian

作者单位: Concordia Institute for Information Systems Engineering Concordia University Montreal,Quebec,H3G 2W1,Canada

会议类型: 国际会议

会议名称: 2009 8th International Conference on Reliability,Maintainability and Safety(第八届中国国际可靠性、维修性、安全性会议)

会议地点: 成都

会议语种:英文

页码: 143-148

在线出版日期: 2009-08-24（万方平台首次上网日期，不代表论文的发表时间）