本文選題：故障診斷　切入點：船舶旋轉機械　出處：《南京航空航天大學》2016年碩士論文　論文類型：學位論文

【摘要】：旋轉機械是工業(yè)領域應用最為廣泛的一類機械設備,也是船舶系統(tǒng)的重要組成部分。由于受到工藝缺陷、安裝不當以及長期惡劣的工作環(huán)境影響,船舶的旋轉機械設備極易發(fā)生故障。這些故障不僅會影響船舶正常航運,還會引發(fā)重大經(jīng)濟損失和人員傷亡。當前船舶系統(tǒng)故障診斷多依賴簡單的檢測設備和專家經(jīng)驗,誤診率較高。因此,針對船舶的旋轉機械設備,研究其故障診斷方法具有重要的理論價值和實際意義。本文結合工程背景,利用基于數(shù)據(jù)驅(qū)動的方法對船舶旋轉機械進行故障診斷,工作主要包括理論分析和應用研究。本文開始概述了故障診斷方法,介紹了基于數(shù)據(jù)驅(qū)動的旋轉機械故障診斷方法的發(fā)展歷程,并分析了現(xiàn)有成果的特點與不足。首先,通過實船監(jiān)測和實驗方式獲得了大量的研究數(shù)據(jù)。實船數(shù)據(jù)采集于航運中的散貨輪,數(shù)據(jù)分為正常狀態(tài)和故障狀態(tài)兩類。實驗數(shù)據(jù)來源于船舶旋轉機械故障模擬實驗平臺,包含了幾種常見故障的數(shù)據(jù)以及正常狀態(tài)下的數(shù)據(jù)。其次,分析了經(jīng)驗模式分解方法在信號分析中的應用,包括對傳統(tǒng)信號降噪方法進行優(yōu)化,以及結合樣本熵理論實現(xiàn)船舶旋轉機械的故障診斷。首先根據(jù)分量相關性原則,提出將遺傳算法與經(jīng)驗模式分解閾值降噪方法相結合,增強降噪后信號與原信號的相關性。然后將經(jīng)驗模式分解與樣本熵理論相結合,完成了船舶旋轉機械的故障診斷。接著,針對傳統(tǒng)信號分析方法去噪難的缺陷,提出了基于灰度圖紋理分析的船舶旋轉機械故障診斷方法。將船舶旋轉機械振動信號轉換為灰度圖像,利用相應的紋理圖像分析方法完成故障識別,并從理論和實驗兩個方面驗證了該方法的可行性和實用性。最后,進行了基于Matlab圖形用戶界面的船舶旋轉機械設備故障診斷系統(tǒng)開發(fā)。介紹了系統(tǒng)的開發(fā)環(huán)境、總體結構設計,說明了各模塊的實現(xiàn)方法,并測試了該軟件系統(tǒng)的有效性和實用性。
[Abstract]:Rotating machinery is one of the most widely used machinery and equipment in industrial field. It is also an important part of ship system. The ship's rotating machinery is prone to malfunction. These faults will not only affect the normal shipping of the ship, but also lead to heavy economic losses and casualties. At present, the fault diagnosis of ship system depends on simple detection equipment and expert experience. Therefore, it is of great theoretical value and practical significance to study the method of fault diagnosis for rotating machinery and equipment of ships. The work of fault diagnosis of ship rotating machinery based on data-driven method mainly includes theoretical analysis and application research. In this paper, the method of fault diagnosis is summarized. This paper introduces the development course of fault diagnosis method for rotating machinery based on data drive, and analyzes the characteristics and shortcomings of existing achievements. A large number of research data are obtained by means of real ship monitoring and experiment. The data are collected from bulk cargo ships in shipping, and the data can be divided into two types: normal state and fault state. The experimental data are derived from the fault simulation experiment platform of ship rotating machinery. It contains several kinds of common fault data and data under normal condition. Secondly, the application of empirical mode decomposition method in signal analysis is analyzed, including the optimization of traditional signal denoising methods. According to the principle of component correlation, the genetic algorithm is combined with empirical mode decomposition threshold to reduce noise. The correlation between the noise reduction signal and the original signal is enhanced. Then the empirical mode decomposition is combined with the sample entropy theory to complete the fault diagnosis of ship rotating machinery. A fault diagnosis method for ship rotating machinery based on grayscale image texture analysis is proposed. The vibration signal of ship rotating machinery is converted into gray image, and the fault identification is accomplished by using the corresponding texture image analysis method. The feasibility and practicability of the method are verified theoretically and experimentally. Finally, the fault diagnosis system of marine rotating machinery equipment based on Matlab graphical user interface is developed. The development environment and overall structure design of the system are introduced. The realization method of each module is explained, and the validity and practicability of the software system are tested.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：U672

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