89. Remaining Life Prediction of Cores Based on Data-driven and Physical Modeling Methods
Xiang Li: Singapore Institute of Manufacturing Technology (SIMTech), Singapore
Wen Feng Lu, Lianyin Zhai: Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore
Meng Joo Er, and Yongping Pan: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
89.1 Introduction
89.2 Weibull Model for Analysis of Time-to-Failure Data in Product Life Cycle Management
Weibull Model for Reliability Analysis
Basics of Weibull Distribution
Weibull Analysis of Life Data: An Illustrative Case Study
89.3 Condition Prediction Using Enhanced Online Learning Sequential-Fuzzy Neural Networks
Architecture of Fuzzy Neural Network
Online Sequential Learning Strategy
Multistep Prediction Scheme
Simulation Studies
89.4 Summary, References
Abstract
This chapter presents development of enabling technologies that are able to assess the reliability of remanufactured products based on predictive modeling methods, to describe fast and accurate prediction algorithms that are able to predict condition of critical components or parts of manufactured products based on historical data. Machine health condition prediction of critical components under the situation of insufficient data, missing prior fault knowledge, and noisy measurement are studied using an enhanced online sequential learning-fuzzy neural network. Meanwhile, Weibull model-based reliability analysis is investigated in this chapter. Performance of various Weibull parameter estimation methods is compared using case studies. Results of this part of research have enabled the development of a product reliability analysis tool that is able to characterize the product failure modes, failure rate, and reliability profile.