Name
Title | ||
|---|---|---|
A Classification Framework for Predicting Components' Remaining Useful Life Based on Discrete-Event Diagnostic Data |
Abstract | ||
|---|---|---|
In this paper, we propose to define the problem of predicting the remaining useful life of a component as a binary classification task. This approach is particularly useful for problems in which the evolution of the system condition is described by a combination of a large number of discrete-event diagnostic data, and for which alternative approaches are either not applicable, or are only applicable with significant limitations or with a large computational burden. The proposed approach is demonstrated with a case study of real discrete-event data for predicting the occurrence of railway operation disruptions. For the classification task, Extreme Learning Machine (ELM) has been chosen because of its good generalization ability, computational efficiency, and low requirements on parameter tuning. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TR.2015.2440531 | Reliability, IEEE Transactions |
Keywords | Field | DocType |
classification task,extreme learning machine,railway rolling stock,remaining useful life prediction,hidden markov models,support vector machines,time series analysis,maintenance engineering,temperature measurement | Time series,Data mining,Binary classification,Extreme learning machine,Support vector machine,Artificial intelligence,Hidden Markov model,Rail transportation,Machine learning,Maintenance engineering,Mathematics | Journal |
Volume | Issue | ISSN |
PP | 99 | 0018-9529 |
Citations | PageRank | References |
4 | 0.45 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olga Fink | 1 | 4 | 0.45 |
| Enrico Zio | 2 | 742 | 57.86 |
| Ulrich Weidmann | 3 | 4 | 0.45 |