發(fā)布時(shí)間：2018-03-03 09:36

  本文選題：物流設(shè)備　切入點(diǎn)：故障診斷　出處：《燕山大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著現(xiàn)代物流的快速發(fā)展，現(xiàn)代物流設(shè)備逐步走向自動(dòng)化和智能化的道路，也使得維護(hù)其安全運(yùn)轉(zhuǎn)的檢測(cè)與維修成為物流設(shè)備管理的重要方面。本文的研究目的是建立基于數(shù)據(jù)挖掘的物流設(shè)備隱性故障預(yù)警模型，估測(cè)隱性故障對(duì)顯性故障發(fā)生的影響值，以此來(lái)實(shí)現(xiàn)監(jiān)控隱性故障變化趨勢(shì)的目的，并對(duì)由隱性故障引起的突發(fā)故障起到一定的預(yù)防作用。針對(duì)課題的特點(diǎn)，本文運(yùn)用歸納分析法對(duì)國(guó)內(nèi)外相關(guān)文獻(xiàn)進(jìn)行總結(jié)；運(yùn)用比較分析法驗(yàn)證算法的有效性；運(yùn)用例證法對(duì)本文提出的隱性故障預(yù)警模型進(jìn)行實(shí)例分析，評(píng)價(jià)模型的可靠性。 首先，從研究背景和意義入手，對(duì)數(shù)據(jù)挖掘技術(shù)、故障診斷技術(shù)、數(shù)據(jù)挖掘技術(shù)在故障診斷中的應(yīng)用等的國(guó)內(nèi)外研究現(xiàn)狀進(jìn)行綜述，得出現(xiàn)有故障診斷技術(shù)的不足之處。并對(duì)物流設(shè)備、故障診斷技術(shù)和數(shù)據(jù)挖掘技術(shù)的相關(guān)基礎(chǔ)知識(shí)進(jìn)行詳細(xì)闡述，奠定本文的理論基礎(chǔ)。 其次，為進(jìn)一步提高顯性故障診斷的效率，并根據(jù)故障診斷過(guò)程中不同故障因素具有不同的故障貢獻(xiàn)度的實(shí)際情況，提出更適用于設(shè)備故障診斷現(xiàn)實(shí)需求的加權(quán)關(guān)聯(lián)增量更新算法。 再次，在對(duì)故障狀態(tài)下與隱性故障相關(guān)的因素進(jìn)行分析的基礎(chǔ)上，提出隱性故障預(yù)警模型，通過(guò)引入神經(jīng)網(wǎng)絡(luò)的方法解決隱性故障這類(lèi)不確定性因素較多的非線(xiàn)性問(wèn)題。該模型不僅實(shí)現(xiàn)了對(duì)顯性故障的診斷，而且通過(guò)對(duì)多種故障因素的綜合分析達(dá)到對(duì)隱性故障進(jìn)行監(jiān)測(cè)的目的。 最后，從結(jié)構(gòu)特點(diǎn)、故障因素等方面對(duì)物流設(shè)備的液壓系統(tǒng)進(jìn)行簡(jiǎn)要分析，選擇具有代表性的故障數(shù)據(jù)建立用于隱性故障預(yù)警的神經(jīng)網(wǎng)絡(luò)，并驗(yàn)證該模型在物流設(shè)備故障診斷中的可行性。
[Abstract]:With the rapid development of modern logistics, modern logistics equipment gradually moving towards automation and intelligent way, also makes the detection and maintenance of the safe operation of the equipment has become an important aspect of logistics management. The purpose of this study is to establish the data mining logistics equipment hidden fault early warning model based on the estimated effect of hidden failures occurred on dominant fault the value, in order to achieve the purpose of monitoring the hidden failure trend, and a preventive role for sudden failure caused by hidden failures. According to the characteristics of the subject, this paper uses the inductive analysis of the related literature at home and abroad were summarized; using the method of comparative analysis verify the effectiveness of the algorithm; analysis of hidden failure warning model the proposed method using examples, the reliability evaluation model.
First of all, starting from the research background and significance of the data mining technology, fault diagnosis technology at home and abroad the status quo of the research of data mining technology in the application of fault diagnosis of the existing shortcomings of fault diagnosis technology. And the logistics equipment, basic knowledge of fault diagnosis technology and data mining technology in detail this laid the theoretical basis of this paper.
Secondly, in order to further improve the efficiency of explicit fault diagnosis, and according to the actual situation of different fault factors with different fault contribution in the process of fault diagnosis, a weighted incremental updating algorithm is put forward which is more suitable for the actual demand of equipment fault diagnosis.
Again, based on the factors associated with the recessive fault condition analysis, put forward the hidden failure warning model and to solve nonlinear problems more hidden failures of this kind of uncertainty factors by introducing a method of neural network. The model not only realizes the diagnosis of dominant fault, and through a comprehensive analysis of the various factors of the fault achieve the purpose of monitoring of hidden failures.
Finally, we briefly analyze the hydraulic system of the logistics equipment from the aspects of structure characteristics and failure factors, select representative fault data, build a neural network for hidden fault early warning, and verify the feasibility of the model in the logistics equipment fault diagnosis.

【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F252;TP311.13

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本文編號(hào)：1560481