Name
Abstract | ||
|---|---|---|
Mechanical devices are playing a crucial role in modern industry. With the ever-growing demands of multiple function and high performance, the unpredicted failures of mechanical device might greatly increase maintenance cost during its lifetime. As a key state indicator of mechanical device, the degradation of some important performance provides substantial information for failure prognosis. More and more attention has been paid to the degradation-based failure time prediction. However, even mechanical devices of the same type might show greatly diverse degradation processes under different working environments. It is still a challenge to identify global degradation pattern and then predict the failure time of a specific mechanical device based on its degradation sequence. This paper proposes a novel approach for failure time prediction with the degradation sequence of mechanical device. The proposed approach combines the exponential regression and parametric empirical Bayesian (PEB) technology. Firstly, exponential regression is adopted to represent the local degradation pattern and then local failure time observations can be computed. Secondly, according to the rule that local failure time observations manifest, appropriate prior assumption is made and the posterior distribution is estimated by PEB technology. Herein, two prior assumptions are considered, including the exchangeable PEB and linear PEB case. The global failure time distribution can be predicted with the estimated prior and posterior distribution. Finally, three case studies are implemented to validate the proposed approach, including the simulation case, crack case and precision case of machine tool. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10845-013-0849-4 | Journal of Intelligent Manufacturing |
Keywords | Field | DocType |
Failure time prediction,Degradation sequence,Mechanical device,Exponential regression,Parametric empirical Bayes | Exponential function,Mechanical devices,Regression,Degradation (geology),Posterior probability,Parametric statistics,Engineering,Reliability engineering,Machine tool,Bayesian probability | Journal |
Volume | Issue | ISSN |
26 | 6 | 0956-5515 |
Citations | PageRank | References |
6 | 0.53 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yuanhang wang | 1 | 6 | 0.53 |
| chao deng | 2 | 6 | 0.53 |
| jun wu | 3 | 6 | 0.53 |
| yao xiong | 4 | 6 | 0.53 |