發(fā)布時間：2018-05-26 04:53

  本文選題：無軌膠輪車 + 全液壓制動系統(tǒng)�。� 參考：《山東科技大學》2017年碩士論文

【摘要】：經(jīng)國內(nèi)外實踐經(jīng)驗表明,礦用無軌運輸設備在現(xiàn)代化礦井的運作中發(fā)揮著重要作用。與傳統(tǒng)的運輸方式相比,礦用無軌運輸設備具有載重量大,速度快,效率高等獨特優(yōu)勢,因此,可以在很大程度上提高礦井運輸能力、生產(chǎn)能力和社會經(jīng)濟效益。其中,無軌膠輪車因其較好的靈活性得到廣泛使用。為了提高行車安全性和制動可靠性,無軌膠輪車一般采用全液壓雙回路制動系統(tǒng)。由于煤礦井下環(huán)境比較惡劣,無軌膠輪車要長期承受巨大的工作負荷,同時在大載荷下車輛還要進行頻繁的啟動與制動等操作,因此制動回路中的制動器、蓄能器、充液閥以及液壓管路等部件會不可避免地出現(xiàn)一些故障,使車輛在運輸過程中失去控制,嚴重時則可能造成礦井人員傷亡,帶來不必要的人財損失。因此,針對無軌膠輪車全液壓制動系統(tǒng)進行故障診斷研究,對于煤礦安全、高效地生產(chǎn)具有重大價值。首先,根據(jù)無軌膠輪車的全液壓制動系統(tǒng)結構組成和重要工作參數(shù),在AMESim環(huán)境下搭建了全液壓制動系統(tǒng)仿真模型,得到前橋蓄能器、后橋蓄能器、前橋制動器、后橋制動器的壓力輸出曲線。結合全液壓制動系統(tǒng)的工作原理,對系統(tǒng)的動態(tài)響應性能進行分析,驗證了所建立AMESim仿真模型的合理性。同時,通過該仿真模型獲得了前后橋蓄能器、前后橋制動器的壓力輸出數(shù)據(jù),以及制動踏板壓力輸入數(shù)據(jù),為下一步進行支持向量機故障診斷提供了可靠的原始數(shù)據(jù)樣本。其次,基于SVM回歸預測故障診斷原理,建立了 4個SVM回歸預測模型,分別對前橋蓄能器壓力、后橋蓄能器壓力、前橋制動器壓力以及后橋制動器壓力進行了訓練和校驗,得到的建模誤差和校驗誤差均在10-2～10-1數(shù)量級,驗證了 SVM模型的推廣性能。再通過所建立的SVM回歸預測模型對故障數(shù)據(jù)進行預測,結果發(fā)現(xiàn)在故障數(shù)據(jù)下得到的殘差值發(fā)生突變,有效診斷出了相應的故障及其發(fā)生時間,驗證了 SVM在全液壓制動系統(tǒng)故障診斷中的可行性。為了進一步增強SVM故障預測模型的診斷性能,有效提高故障診斷正確率,利用交叉驗證、遺傳算法以及粒子群算法分別對SVM故障預測模型的核參數(shù)g以及懲罰參數(shù)c進行優(yōu)化。在最佳參數(shù)g和參數(shù)c下再次對SVM回歸模型進行訓練與預測,得到診斷效果更優(yōu)的SVM故障預測模型,其預測精度由原來的10-3提升到10-5數(shù)量級,結果令人滿意。最后,基于Labwindows/CVI軟件設計了 一套全液壓制動系統(tǒng)狀態(tài)監(jiān)測與故障診斷系統(tǒng),通過友好的人機界面對液壓制動系統(tǒng)的狀態(tài)進行實時監(jiān)控,并利用ActiveX技術調(diào)用MATLAB支持向量機故障預測模型程序,實現(xiàn)了對全液壓制動系統(tǒng)的狀態(tài)監(jiān)測與故障診斷。
[Abstract]:The practical experience at home and abroad shows that the trackless transport equipment plays an important role in the operation of modern mines. Compared with the traditional transportation mode, the mine trackless transportation equipment has the unique advantages of large load, high speed and high efficiency, so it can greatly improve the mine transportation capacity, production capacity and social and economic benefits. Among them, trackless rubber wheel car is widely used because of its good flexibility. In order to improve driving safety and braking reliability, trackless rubber wheel cars generally adopt full hydraulic double-loop braking system. Because the underground environment of coal mine is relatively bad, the trackless rubber wheel car has to bear a huge workload for a long time, and at the same time, the vehicle has to carry on frequent operation such as starting and braking under the heavy load, so the brake and accumulator in the brake circuit, Some faults will inevitably occur in the hydraulic valve and hydraulic pipeline, which will make the vehicle out of control during the transportation process, and may cause mine personnel casualties and unnecessary loss of human wealth when serious. Therefore, the research of fault diagnosis for the full hydraulic braking system of trackless rubber wheel car is of great value to the safe and efficient production of coal mine. First of all, according to the structure composition and important working parameters of the full hydraulic brake system of the trackless rubber wheel car, the simulation model of the full hydraulic braking system is built under the AMESim environment, and the front axle accumulator, the rear axle accumulator and the front axle brake are obtained. Pressure output curve of rear axle brake. Combined with the working principle of the full hydraulic braking system, the dynamic response performance of the system is analyzed, and the rationality of the established AMESim simulation model is verified. At the same time, the output data of the front and rear axle accumulator, the pressure output of the front and rear axle brake and the input data of the brake pedal pressure are obtained through the simulation model, which provides a reliable original data sample for the next step in the fault diagnosis of support vector machine. Secondly, based on the principle of SVM regression prediction fault diagnosis, four SVM regression prediction models are established. The pressure of front axle accumulator, rear axle accumulator, front axle brake and rear axle brake are trained and calibrated respectively. Both the modeling error and the calibration error are in the order of 10 ~ (-2) ~ 10 ~ (-1), which verifies the extended performance of the SVM model. Then the fault data are predicted by the established SVM regression prediction model. The results show that the residual value under the fault data has a sudden change, and the corresponding fault and its occurrence time are effectively diagnosed. The feasibility of SVM in fault diagnosis of full hydraulic braking system is verified. In order to further enhance the diagnosis performance of SVM fault prediction model and improve the accuracy of fault diagnosis effectively, the kernel parameters g and penalty parameter c of SVM fault prediction model are optimized by cross validation, genetic algorithm and particle swarm optimization algorithm, respectively. Under the optimal parameters g and c, the SVM regression model is trained and predicted again, and a SVM fault prediction model with better diagnostic effect is obtained. The prediction accuracy is improved from 10-3 to 10-5, and the results are satisfactory. Finally, a full hydraulic braking system condition monitoring and fault diagnosis system is designed based on Labwindows/CVI software. The condition of hydraulic braking system is monitored in real time through friendly man-machine interface. The condition monitoring and fault diagnosis of full hydraulic braking system are realized by calling the MATLAB support vector machine fault prediction model program with ActiveX technology.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TD525

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本文編號：1936011