本文選題：渦軸發(fā)動(dòng)機(jī)　切入點(diǎn)：故障診斷　出處：《南京航空航天大學(xué)》2016年碩士論文

【摘要】：渦軸發(fā)動(dòng)機(jī)FADEC系統(tǒng)中傳感器和執(zhí)行機(jī)構(gòu)直接影響控制及整個(gè)發(fā)動(dòng)機(jī)的安全穩(wěn)定運(yùn)行。為了保障渦軸發(fā)動(dòng)機(jī)可靠性,本文開展了執(zhí)行機(jī)構(gòu)及其傳感器故障診斷、氣路傳感器故障診斷以及容錯(cuò)控制相關(guān)研究。本文基于渦軸發(fā)動(dòng)機(jī)非線性部件級(jí)模型開展上述研究工作,首先采用具有計(jì)算發(fā)散判定和校正機(jī)制的有限次通過(guò)改進(jìn)Broyden擬牛頓法替代牛頓一次通過(guò)法,提高部件級(jí)模型動(dòng)態(tài)過(guò)程計(jì)算實(shí)時(shí)性和精度。構(gòu)建了基于執(zhí)行機(jī)構(gòu)模型和發(fā)動(dòng)機(jī)逆模型的執(zhí)行機(jī)構(gòu)及其傳感器故障診斷系統(tǒng)。采用傳遞函數(shù)結(jié)合擬合法建立執(zhí)行機(jī)構(gòu)數(shù)學(xué)模型;提出了一種自校正學(xué)習(xí)速率的在線BP神經(jīng)網(wǎng)絡(luò)算法,并應(yīng)用于發(fā)動(dòng)機(jī)逆模型的建立。通過(guò)數(shù)字仿真表明診斷系統(tǒng)對(duì)于執(zhí)行機(jī)構(gòu)故障以及LVDT傳感器故障能夠?qū)崿F(xiàn)準(zhǔn)確的定位和診斷,同時(shí)具有較好的適應(yīng)能力。針對(duì)氣路傳感器故障診斷問(wèn)題,分別采用基于自適應(yīng)模型和基于在線極端學(xué)習(xí)機(jī)的診斷系統(tǒng)。提出一種基于方程組求解的自適應(yīng)模型建立方法,具有較高的精度和自適應(yīng)能力,并應(yīng)用于傳感器故障診斷;采用在線極端學(xué)習(xí)機(jī)算法對(duì)各氣路傳感器建立智能映射模塊形成診斷系統(tǒng),并引入“預(yù)學(xué)習(xí)”思路,使得該方法對(duì)于傳感器單故障、雙故障均具有出色的診斷能力和較高的重構(gòu)精度,同時(shí)具有較好的實(shí)時(shí)性和魯棒性。在實(shí)現(xiàn)傳感器故障診斷后,繼續(xù)對(duì)容錯(cuò)控制開展研究。設(shè)計(jì)了LVDT傳感器以及氣路傳感器故障隔離方案;采用增廣LQR方法設(shè)計(jì)多組控制器,并提出一種扭矩指令模型,在此基礎(chǔ)上采用控制回路切換的方式實(shí)現(xiàn)主動(dòng)容錯(cuò)控制,提高控制系統(tǒng)穩(wěn)定性。最后,針對(duì)執(zhí)行機(jī)及其傳感器故障診斷系統(tǒng)以及基于在線極端學(xué)習(xí)機(jī)的氣路傳感器故障診斷系統(tǒng)設(shè)計(jì)半物理仿真試驗(yàn),驗(yàn)證了算法的有效性。
[Abstract]:The sensors and actuators in the FADEC system of the swirl shaft engine directly affect the control and the safe and stable operation of the whole engine.In order to ensure the reliability of the vortex shaft engine, the related research on actuator and its sensor fault diagnosis, gas path sensor fault diagnosis and fault tolerant control are carried out in this paper.In this paper, based on the nonlinear component level model of the turbo-shaft engine, the finite order method with the mechanism of calculating divergence determination and correction is used to replace the Newtonian one-pass method by improving the Broyden quasi-Newton method.Improve the real-time and precision of dynamic process calculation of component-level model.The actuator and its sensor fault diagnosis system based on the actuator model and the engine inverse model are constructed.An online BP neural network algorithm for self-tuning learning rate is proposed, which is applied to the establishment of the inverse model of the engine, and the mathematical model of the actuator is established by using the transfer function combined with the fitting method, and an on-line BP neural network algorithm with self-tuning learning rate is proposed.The results of digital simulation show that the diagnosis system can accurately locate and diagnose the actuator faults and LVDT sensor faults, and has a good adaptability at the same time.To solve the problem of gas path sensor fault diagnosis, the diagnosis system based on adaptive model and online extreme learning machine is adopted respectively.An adaptive model building method based on solving equations is proposed, which has high accuracy and adaptive ability, and is applied to sensor fault diagnosis.The on-line extreme learning machine algorithm is used to establish an intelligent mapping module for each gas path sensor to form a diagnosis system, and the idea of "pre-learning" is introduced to make the method for the single fault of the sensor.Both faults have excellent diagnosis ability, high reconstruction accuracy, good real-time performance and robustness.After the sensor fault diagnosis is realized, the research on fault tolerant control is carried out.The fault isolation scheme of LVDT sensor and gas sensor is designed, and the method of augmented LQR is used to design multi-group controller, and a torque instruction model is proposed. On the basis of this, active fault-tolerant control is realized by switching control loop.Improve the stability of the control system.Finally, a semi-physical simulation test is designed for the fault diagnosis system of the actuator and its sensors and the gas sensor fault diagnosis system based on the on-line extreme learning machine, which verifies the effectiveness of the algorithm.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：V263.6

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