本文關(guān)鍵詞： 電梯故障診斷 BP神經(jīng)網(wǎng)絡(luò) 模糊系統(tǒng) 遺傳算法　出處：《東北大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代化城市的快速發(fā)展,電梯已成為不可缺少的配套工具。電梯的安全性直接關(guān)系到人身安全,及時發(fā)現(xiàn)故障和及時檢修故障是確保電梯可靠運行、提高電梯監(jiān)控技術(shù)水平和保證人身安全的關(guān)鍵。 電梯控制系統(tǒng)在交流變頻變壓調(diào)速電梯系統(tǒng)中是最重要的部分,也是電梯系統(tǒng)中故障頻發(fā)的系統(tǒng),目前電梯故障診斷方法一般是依靠技術(shù)人員的感覺和經(jīng)驗,難以確保故障診斷的快速性和精準(zhǔn)性。針對這一現(xiàn)狀,本文展開了電梯控制系統(tǒng)故障診斷的研究,目的在于當(dāng)電梯發(fā)生故障時能及時診斷出故障,同時能及時采取相應(yīng)的措施排除故障,保證人員和設(shè)備的安全。 首先,整個電梯系統(tǒng)及其復(fù)雜,所采集的狀態(tài)參數(shù)非常龐大,本文只對電梯控制系統(tǒng)進行研究。電梯控制系統(tǒng)故障信號的不確定性、模糊性及非線性,同時考慮到采集數(shù)據(jù)具有周期性,各個參數(shù)的量綱也不同,本文將模糊系統(tǒng)和BP神經(jīng)網(wǎng)絡(luò)進行結(jié)合的辦法,采用串聯(lián)結(jié)合方式；先將狀態(tài)信號進行模糊化處理,將模糊化后的信號輸入到神經(jīng)網(wǎng)絡(luò),組成模糊神經(jīng)網(wǎng)絡(luò)的故障診斷方法。 利用遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的連接權(quán)值和閾值,用優(yōu)化得到的權(quán)值和閾值作為神經(jīng)網(wǎng)絡(luò)的初始權(quán)值和閾值對網(wǎng)絡(luò)重新進行訓(xùn)練,仿真結(jié)果表明遺傳算法優(yōu)化的BP網(wǎng)絡(luò)明顯優(yōu)于傳統(tǒng)的BP網(wǎng)絡(luò)。 最后完成了電梯遠程監(jiān)控與故障在線診斷系統(tǒng)的設(shè)計與實現(xiàn)。 本文研究成果對其他類似系統(tǒng)有一定的參考價值。
[Abstract]:With the rapid development of modern cities, elevators have become an indispensable supporting tool. The safety of elevators is directly related to personal safety. Timely detection of faults and timely maintenance of faults is to ensure the reliable operation of elevators. Improve the elevator monitoring technology and ensure the key to personal safety. Elevator control system is the most important part of AC variable-frequency variable-voltage adjustable speed elevator system, and it is also the frequently occurring system in elevator system. At present, the method of elevator fault diagnosis generally depends on the feeling and experience of technicians. It is difficult to ensure the rapidity and accuracy of the fault diagnosis. In view of this situation, the research of elevator control system fault diagnosis is carried out in this paper, the purpose is to diagnose the fault in time when the elevator fault occurs. At the same time, can take timely measures to troubleshoot, to ensure the safety of personnel and equipment. First of all, the whole elevator system and its complexity, the collected state parameters are very large, this paper only study the elevator control system. Elevator control system fault signal uncertainty, fuzziness and nonlinearity. At the same time, considering the periodicity of the collected data and the different dimensions of each parameter, the method of combining fuzzy system with BP neural network is adopted in this paper. Firstly, the state signal is processed by fuzzification, and the fuzzy signal is input into the neural network to form the fault diagnosis method of the fuzzy neural network. Genetic algorithm is used to optimize the connection weight and threshold of BP neural network, and the optimized weights and thresholds are used as the initial weights and thresholds of the neural network to re-train the network. The simulation results show that the BP network optimized by genetic algorithm is obviously superior to the traditional BP network. Finally, the design and implementation of elevator remote monitoring and fault online diagnosis system are completed. The research results of this paper have certain reference value to other similar systems.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TU857;TP277

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本文編號：1450067