Predictive modeling for soft measurement of loader driveshaft torque based on large-scale distributed data

Abstract

In view of the harsh working environment, high rotational speed and limited installation space of the loader drive shaft, etc. In this study, a soft measurement method of loader drive shaft torque with distributed sparse least squares support vector machine (DSLS-SVM) algorithm for large-scale data is proposed. Firstly, by analyzing the loader power transmission path, the features for predicting torque are determined. Secondly, irrelevant or redundant features are eliminated using the maximum information coefficient (MIC). Thirdly, the DSLS-SVM algorithm is tuned with the cuckoo search (CS) algorithm to realize the training and testing of the soft measurement model. Finally, to validate the feasibility and reliability, operating data are obtained from new in-service loaders. The test results show that the soft measurement method proposed can effectively characterize the torque under the operating condition, and is suitable for the long-term operating condition monitoring of the loader, which has good application value.