RESUMEN
Spectrometric oil analysis is an important method to study the running state of Power-Shift Steering Transmission (PSST). A method of multiple out least squares support vector regression was developed using spectrometric oil analysis data and SVM (Support Vector Machine). The spectrometric oil analysis data were studied using multiple out least squares support vector regression. It has been proved that the regression data are good in approximation effect for No. 1 PSST. And the predictive values for No. 2 PSST are highly veracious with the test data. The fault information was found and the fault position was determined through compar4tive analysis. This method has been proved to have practice significance for finding fault-hidden dangers and judging fault positions.
RESUMEN
Spectrometric oil analysis is an important method to study the running state of power-shift steering transmission (PSST). An evaluation model of PSST health state was developed on the basis of the theories of principal component analysis (PCA) and analytic hierarchy process (AHP) using spectrometric oil analysis data. Considering the concept of mechanical equipment and wear elements in spectrometric oil analysis data, the health value was employed to quantitatively describe the running state degree of PSST, and the grades of health state were classified based on the health values. The oil analysis data were studied during the process of choosing principal components. The weight vectors of principal components were obtained by using the AHP method. In the course, the conformation of judgement matrix and the consistency check were also studied. The evaluation model was developed by combining the PCA and AHP methods. This model has been proved to have better accuracy in evaluating the running state of PSST. This work is important for developing state evaluation of PSST.