Estimating highlead logging performance through statistical models Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/cf95jf54n

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  • The study of highlead logging operations through the use of statistical models is investigated, and their potential use for estimating and control of operations examined. During the preliminary phases of the study 16 work elements and 26 influencing variables are identified and measurement criteria developed for each. A coding system is also developed to aid recording the variables previously identified in a manner understandable to an electronic computer. Based on the variables and elements to be measured and recorded, data sheets and field procedures are developed and tested. It was proven necessary to use a two man field team to insure that all of the data on each turn, or cycle, was recorded accurately. The procedures necessary to convert the field data into computer input are explained and illustrated with a set of data sheets and summary sheet. The computing methods used in a stepwise regression analysis are discussed in general and its effects on selected lists of variables for each element are shown and discussed. Statistical models of each of the ten regular elements are computed from data taken on 590 turns, or cycles. The resulting models are then analyzed as to their effect on total cycle time and their relation to the form expected by logging estimators and supervisors. The majority of the deviations from expected form are explained but several remain unresolved, and pose a problem for future study. The frequency, mean duration, and proportion of total time consumed by the six irregular elements are computed and their relation to usual job efficiency experienced on similar operations is examined and found to be of the same order. Ten statistical models for the regular elements are recombined into gross element models to provide more convenient data for field use. The gross element and total cycle models were recomputed from the original field data using the variables from the appropriate element models. The resulting gross element models are then tabularized and sample sheets included to illustrate the general procedure for determining cycle times from the tabular data and correction factors. The reliability of the existing models is discussed and it is shown that it is impossible to develop statistical measures of spread or deviation from the computed regression line from the existing data. The measures of the probability of observing and computing a model on only a chance variation are very small, all are less than 2.5 percent. Because of this the conclusion is drawn that causal relationships do exist in highlead logging. To calibrate the existing models an independent data sample is needed. The multivariate calibration procedures necessary are identified and a proposed computing method to reduce the size of the confidence belt is discussed. The study pointed out several areas for future study. They are: 1. The present element and gross element models should have confidence limits placed on them. 2. Several of the models indicate higher than expected standard error. These should be studied for possible improvements. 3. The range of the variables should be enlarged as soon as possible to reflect conditions in other logging areas. 4. A cause analysis study be instituted to study machine breakdown history and causes. 5. Development of a training manual for future investigators to reduce the instruction time and enable them to better answer questions from the men in the field. 6. A comparison study of the results predicted by the models with the estimates and historical records of logging. performance. 7. Locate or develop the necessary computer programs to allow full computer estimation of highlead performance using the models, topographic maps and timber cruise data.
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