Name
Abstract | ||
|---|---|---|
Recently, Prognostics and Heath Management techniques have been deeply investigated with the aim to reduce life-cycle cost of products and systems. The increasing availability of condition monitoring data in substantial quantities for multitudes of homogeneous products and the need for generic algorithms that are applicable to complex systems motivates the development of new data-driven prognostic approaches. In this paper, two data-driven algorithms, one based on a statistical approach and another based on Neural Network, are discussed and tested for an application case. The analysis of the results has shown that both the considered approaches are characterized by reliable prediction performances on Remaining Useful Life calculation, thus resulting as potential tools for the application of a Condition-Based Maintenance strategy. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/I2MTC.2016.7520371 | 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings |
Keywords | Field | DocType |
prognostics and health management,data-driven algorithms,fleet management,condition-based maintenance | Complex system,Maintenance strategy,Condition-based maintenance,Data-driven,Prognostics,Condition monitoring,Artificial neural network,Fleet management,Mathematics,Reliability engineering | Conference |
ISBN | Citations | PageRank |
978-1-4673-9221-1 | 0 | 0.34 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Loredana Cristaldi | 1 | 45 | 14.08 |
| Giacomo Leone | 2 | 0 | 0.68 |
| Roberto Ottoboni | 3 | 48 | 18.94 |
| Subanatarajan Subbiah | 4 | 17 | 3.62 |
| simone turrin | 5 | 1 | 0.73 |