Title: Grey GM(1,1) Model with Function-Transfer Method for Wear Trend Prediction and its Application
Abstract: Trend forecasting is an important aspect in fault diagnosis and work state super-vision. The principle, where Grey theory is applied in fault forecasting, is that the fore-cast system is considered as a Grey system; the existing known information is used to infer the unknown in f ormations character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whiteniza tion of a Grey process. But the traditional equal time interval Grey GM (1,1) model re-quires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non-equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model,but its requirement is still higher and the data were pre-processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establi-shes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre-process the primitive data. It is not only suited for equal interval data modeling, but also for non-e-qual interval data modeling. Its calculation is simple and convenient to use. The oil spec-trum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The exam ple shows that the two models are simple and practical, and worth expanding and apply-ing in plant fault diagnosis.
Publication Year: 2001
Publication Date: 2001-01-01
Language: en
Type: article
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