Sustainable Manufacturing Oriented Prognosis for Facility Reuse

Abstract:

Reuse is considered as one of the most reasonable strategies to realize sustainability. This paper presents an efficient methodology of ann-based prognosis combined with reliability methods to evaluate and guarantee the reusability of facility. The methodology provides the prediction of the remaining life of facility utilizing artificial neural networks. Corresponding reliability is calculated through fitting Weibull distribution to in-house time-to-failure data. Maintenance decision is made by predicting the time when the reliability or remaining life of facility reach the thresholds decided by facility’s reusability. Application results show that the proposed methodology provides effective condition information for reuse decision making from both historical and on-line perspectives.

• Periodical: Key Engineering Materials (Volume 450)
• Pages: 437-440
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.450.437
• Online since: November 2010
• Authors: Ji Hong Yan, Ding Guo Hua, Xing Wang
• Keywords: Maintenance, Prognosis, Reliability, Reusability, Sustainable Manufacturing

References

[1] Aiwina Heng, Sheng Zhang, Andy C.C. Tan, and Joseph Mathew: Rotating machinery prognostics: State of the art, challenges and opportunities, Mechanical Systems and Signal Processing Vol. 23 (2009), pp.724-739.

DOI: 10.1016/j.ymssp.2008.06.009

[2] You-Tern Tsai, Kuo-Shong Wang, and Hwei-Yuan Teng: Optimizing preventive maintenance for mechanical components using genetic algorithms, Reliability Engineering and System Safety Vol. 74 (2001).

DOI: 10.1016/s0951-8320(01)00065-5

[1] 2.

[1] [2] lT.