GM(1,1) Modeling of Failure Rate Prediction for Preventive Maintenance

Hong-Xia Chen ； Guang-Jun Jiang ； Qing-Chao Zhang

《International Journal of Information and Management Sciences》 29卷4期 (2018/12) Pp. 365-379

https://doi.org/10.6186/IJIMS.201812_29(4).0002

摘要

In preventive maintenance, the prediction of failure rate is of great significance for making accurate and reasonable maintenance plan, mastering the initiative of maintenance and giving full play to the use efficiency of equipment. The traditional failure rate methods and models rely on a large amount of statistics. However, statistics data are often unavailable, which creates difficulties in predicting failure rates. Aiming at the current situation of ＂little data, poor information＂, this paper summarizes existing research on failure rate prediction methods at home and abroad, and the GM(1, 1) model is used to predict the failure rate. This article analyzes the concrete examples of cranes. Firstly, the GM(1, 1) model and the discrete GM(1, 1) model are established based on the determination of the length of the model for the fault rate curve with large irregular fluctuation, and the models is compared and optimized. Secondly, the linear regression GM(1, 1) model is introduced for the curve with linear trend, and three models are compared and optimized. It has practical application value in the prediction of equipment failure rate.

關鍵字

Failure rate forecast ； GM(1, 1) model ； discrete GM(1, 1) model ； linear regression GM(1, 1) model

參考文獻

Chen, C. and Ki, G. and (2013). Study and identify on the best reserve quota of maintenance spares, Applied Mechanics and Materials, Vol.275, No.277, 2697-2700.

Gao, Q. (2013). Research on Condition Monitoring and Fault Diagnosis of Large Tonnage All Terrain Crane, Xián: ChangÁn University, June, 34-51.

Hu, S. T. (2016). Reliability Evaluation and Allocation of A Large Mining Excavator Based on Failure and Maintenance Data, Chengdu: University of Electronic Science and Technology of China, June, 43-57.

Jiang, T. and Liu, Y. (2017). Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences, Reliability Engineering and System Safety, Vol.2017, 3-15.

Jaleel, J A. and Sabina, M F. (2013). Reliability analysis using fault tree method and its predic- tion using neural networks, Control; Communication and Computing (ICCC), 2013 International Conference, 119-124.