Fault diagnosis of wet clutch control system of tractor hydrostatic power split continuously variable transmission

Abstract

A hydrostatic power split continuously variable transmission (CVT) for a tractor often has multiple ranges to improve transmission efficiency. However, a failure of the wet-clutch control system will lead to interruption of tractor power and even endanger driving safety. To improve the reliability of CVT tractors, methods for diagnosing faults in the wet-clutch control system were studied. A test bench was built and the clutch engagement pressure was measured under different fault modes. For these time series data, statistics such as the mean, variance, kurtosis etc. were selected as features in the analysis. An improved Gaussian naive Bayes algorithm based on a time window as well as principal component analysis were used to classify the different fault modes of the clutch control system. Finally, the performance of the algorithm was analyzed. A test with multiple faults was run, as was a comparison with traditional algorithms. By optimizing the time window for data interception, the classification accuracy of the Gaussian naive Bayes algorithm for normal operation reached 97% and reached 100% for the fault modes. The average accuracy and recall rate of fault diagnosis were 98.2% and 89.4%, respectively, which are better than the results for a support vector machine, the k-nearest neighbors algorithm, or the decision tree algorithm. Importantly, the results show that the clutch pressure fluctuations during the shift by a tractor CVT can be used for the fault diagnosis of a clutch control system.