【摘要】：伴隨著高層建筑的興建,電梯使用范圍越來越廣,而且電梯安全問題也日漸嚴重,加強電梯安全檢測成為了當務之急。因此,設(shè)計一臺便攜、多參數(shù)的電梯安全檢測裝置具有重要的現(xiàn)實意義。本文在查閱了國內(nèi)外電梯安全檢測相關(guān)產(chǎn)品的基礎(chǔ)上,結(jié)合項目需求,設(shè)計了一臺電梯多參數(shù)安全檢測裝置,完成了原理樣機的制作與測試。本文主要工作和特色如下:(1)所設(shè)計的電梯多參數(shù)安全檢測裝置由上下位機組成。下位機使用嵌入式系統(tǒng)設(shè)計,負責采集和處理電梯的三軸加速度、運行速度、位移、振動、轎門速度、噪聲和電機功率等數(shù)據(jù);上位機采用平板電腦,負責顯示、存儲測量結(jié)果以及與遠程服務器通信。整套裝置實現(xiàn)了多參數(shù)在線檢測,且靈活便攜,人機交互友好。(2)裝置集成了平衡系數(shù)測量功能和故障診斷功能,一機多用。(3)對裝置的原理樣機進行了實驗室測試與現(xiàn)場測試,結(jié)果表明各功能模塊運行正常,達到了設(shè)計要求。(4)介紹了基于貝葉斯網(wǎng)絡的故障診斷方法,重點研究了貝葉斯網(wǎng)絡的推理和參數(shù)學習過程,并結(jié)合電梯加速度故障診斷實例進行說明,最后以仿真實驗驗證。
[Abstract]:With the construction of high-rise buildings, elevators are used more and more widely, and elevator safety problems are becoming more and more serious, so it is urgent to strengthen elevator safety detection. Therefore, it is of great practical significance to design a portable and multi-parameter elevator safety detection device. In this paper, on the basis of consulting the related products of elevator safety testing at home and abroad, combined with the requirements of the project, a multi-parameter safety detection device of elevator is designed, and the manufacture and test of the prototype are completed. The main work and characteristics of this paper are as follows: (1) the designed elevator multi-parameter safety detection device is composed of upper and lower computers. The lower computer is designed by embedded system, which is responsible for collecting and processing the data of elevator triaxial acceleration, running speed, displacement, vibration, sedan chair door speed, noise and motor power. The upper computer adopts tablet computer, which is responsible for displaying, storing the measurement results and communicating with the remote server. The whole device realizes multi-parameter on-line detection, flexible and portable, human-computer interaction friendly. (2) the device integrates balance coefficient measurement function and fault diagnosis function, one machine is multi-purpose. (3) the prototype of the device is tested in laboratory and tested on the spot. The results show that each functional module runs normally and meets the design requirements. (4) the fault diagnosis method based on Bayesian network is introduced. The reasoning and parameter learning process of Bayesian network are studied in detail, and the example of elevator acceleration fault diagnosis is given to illustrate it. Finally, the simulation experiment is used to verify it.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TU857

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本文編號：2509272