Lifting State Detection of Oil–Gas Jack-Up Platform Based on Improved Random Forest

In order to improve the accuracy of the lifting state detection of an oil–gas jack-up platform, a lifting state detection method based on improved random forest is proposed to solve the problems of low detection efficiency caused by the interference of lifting data and redundant features. To detect outliers in the lifting data of an oil–gas jack-up platform by the K-means clustering method and clean abnormal data, the principal component analysis method is introduced into the random forest algorithm to reduce the dimension of lifting data, and an improved random forest is constructed with the Gini impurity criterion to preliminarily classify the lifting state. Then, fuzzy comprehensive evaluation is used to refine the state of the classification result and realize lifting state detection. The test results show that the proposed method has good stability in lifting state timing detection, a high-inter class-to-intra-class distance ratio, and accurate platform displacement detection under different incident angles/motion responses.