拖拉机液压机械无级变速箱换段过程液压故障诊断

Hydraulic failure diagnosis of tractor hydro-mechanical continuously variable transmission in shifting process

为了提高拖拉机液压机械无级变速箱换段品质的稳定性和换段系统的工作可靠性,提出一种针对其离合器液压系统的故障诊断方法。该文基于南京农业大学自主研发的液压机械无级变速箱,通过试验获得正常及4种典型故障模式下换段过程的60组压力、流量数据。基于Fisher准则与核方法,对处理后的样本进行学习,并基于粗糙集理论与特征向量法,分别对样本的属性集及训练样本集进行精简。通过随机选取训练样本集与测试样本集并进行多次分类取平均的方法对算法的鲁棒性进行了测试。结果表明,针对全部60组试验样本,所使用算法可实现正常及4种故障模式的正确分类,且在随机样本测试中,正常及4种典型故障模式下的算法平均识别率分别为99.67%、100%、99%、98%、99%,具有较高的鲁棒性。该研究对提高液压机械无级变速箱的自动化水平及推动其在国产拖拉机中的应用提供了参考。

Unlike conventional mechanical transmission with manual shift, the hydro-mechanical CVT（continuously variable transmission） shifts from one range to another automatically under the control of hydraulic system, however, some faults of this hydraulic system can affect the establishment of clutch engagement pressure and affect the shift quality further. In order to improve the reliability of the hydro-mechanical CVT in shifting process and avoid safety accident, the problem of fault diagnosis on this hydraulic system was studied in this paper. First, a measurement and control system for the hydro-mechanical CVT was built, including the transmission, clutch hydraulic control system, sensors, transmission control unit and IPC（industrial personal computer）. Software developed under Labview can build the communication between the IPC and the sensors, and record the feedback data from sensors to the table file with a frequency of 62.5 Hz. Then, 5 fault modes were defined in this paper, i.e. normal state, piston seizure, seal ring damage, oil passages clog and pipeline sealing failure. Through experiments, 60 groups of feedback data from pressure gauges and flowmeter with different fault modes were acquired. Thirty groups of the testing data were selected as training samples and the other 30 groups were selected as testing samples. For each sample, 6 kinds of statistic features such as root mean square value of sample data in a second were calculated. Because any combination of the 6 statistic features could not classify the fault sample directly in a low-dimensional space, it was necessary to map the sample to a higher dimensional space and to find the hyperplane of demarcation. So, a projection vector was calculated using the method of kernel based on the rule of Fisher. According to this projection vector, the sample data which was difficult to classify in original space could be transferred to feature space, in which the sample data could be classified with a simple linear function. After the kernel calculation, the methods to reduce the computation complexity were studied. Based on the rough set theory, the kernel features were found and the FAI（fisher based attribute importance function） value was calculated. The kernel features and the non-kernel features with a high FAI value were selected as the training features. Based on the heuristic algorithm, the training sample data corresponding to the minimum component in projection vector could be deleted in turn. Finally, in order to test the robustness of the algorithm, 10 groups of training set and testing set were rebuilt through random sample selection. The results revealed that the training and testing sample data could be classified correctly using the method of kernel, and proper reduction on features and training samples could reduce the computation complexity of fault diagnosis without accuracy loss. In addition, The average fault diagnostic accuracies of the random samples under the 5 modes were 99.67%, 100%, 99%, 98% and 99%, respectively, and the variation range for each group of sample was from 96.7% to 100%. The results indicated a high robustness of the algorithm used in this paper. The study of this paper provides a method to improve the reliability of the hydro-mechanical CVT.