本文選題：純電動載貨汽車 + 故障診斷�。� 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：傳統(tǒng)載貨汽車能耗高、污染大,不符合21世紀(jì)低碳綠色的經(jīng)濟(jì)發(fā)展趨勢。純電動載貨汽車的出現(xiàn)很好地解決了上述問題。然而純電動載貨汽車面世時間短,一些關(guān)鍵部件比較容易發(fā)生故障,加上缺乏相關(guān)維修經(jīng)驗(yàn)和技術(shù)人員,經(jīng)常會導(dǎo)致車輛出現(xiàn)故障無法及時維修。因此為純電動載貨汽車的關(guān)鍵部件開發(fā)故障診斷專家系統(tǒng)具有迫切的需求。本文依托四川省科技廳科技支撐計(jì)劃項(xiàng)目——“純電動載貨汽車集成關(guān)鍵技術(shù)研究及示范(五大高端)”(項(xiàng)目編號:2016GZ0020)的支持,開展針對純電動載貨汽車關(guān)鍵部件的故障診斷專家系統(tǒng)研究。本文首先重點(diǎn)分析了純電動載貨汽車兩大關(guān)鍵部件——電池系統(tǒng)和永磁同步電機(jī)的結(jié)構(gòu)和工作原理。接著搜集并整理出了這兩個部件的常見故障,通過建立故障樹梳理了各個故障事件的層次關(guān)系,減少了設(shè)計(jì)知識庫時的冗余。由于電池系統(tǒng)和永磁同步電機(jī)的故障與故障征兆具有明顯的因果關(guān)系,因此本文采用產(chǎn)生式規(guī)則表示法建立了知識庫;通過對比分析幾種模糊推理方法的優(yōu)缺點(diǎn)并結(jié)合純電動載貨汽車的實(shí)際情況,應(yīng)用貝葉斯網(wǎng)絡(luò)構(gòu)建推理機(jī);通過因果關(guān)系調(diào)查表判定故障和故障征兆之間的因果關(guān)系,進(jìn)而構(gòu)造貝葉斯網(wǎng)絡(luò),并確定在某些故障發(fā)生與否的情況下對應(yīng)于某一故障征兆的條件概率。由于專家在確定條件概率時具有很強(qiáng)的主觀性,由此帶來的誤差可能影響診斷結(jié)果的準(zhǔn)確度,因此本文在設(shè)計(jì)推理機(jī)時提出了一種通過對歷史診斷記錄進(jìn)行數(shù)據(jù)挖掘?qū)崿F(xiàn)診斷結(jié)果輔助決策的方法,使得專家系統(tǒng)能夠隨著使用次數(shù)的增多逐漸提高診斷的準(zhǔn)確度。最后本文使用Java語言設(shè)計(jì)實(shí)現(xiàn)了一套B/S結(jié)構(gòu)的故障診斷專家系統(tǒng),該專家系統(tǒng)僅需使用瀏覽器登錄便可使用,具有極大的便利性。本文充分利用貝葉斯推理理論,通過開發(fā)專家系統(tǒng)為純電動載貨汽車關(guān)鍵部件的故障診斷提出了新思路,具有較高的經(jīng)濟(jì)價值。此外,應(yīng)用數(shù)據(jù)挖掘技術(shù)優(yōu)化故障診斷專家系統(tǒng),一定程度上解決了專家系統(tǒng)知識獲取困難的問題。本專家系統(tǒng)還具有較強(qiáng)的通用性,可以適用于其他領(lǐng)域的因果關(guān)系不確定推理。
[Abstract]:Traditional truck has high energy consumption and high pollution, which does not accord with the economic development trend of low carbon green in the 21 ~ (st) century. The emergence of pure electric truck solves the above problem well. However, due to the short arrival time of pure electric truck, some key parts are easy to break down, and the lack of relevant maintenance experience and technical personnel will often lead to vehicle failure and failure in time. Therefore, it is urgent to develop fault diagnosis expert system for the key parts of pure electric truck. This paper relies on the support of the Science and Technology support Program of Sichuan Provincial Science and Technology Department-"Research and demonstration of key Technologies in the Integration of Pure Electric truck (five High end)" (Project No.: 2016GZ0020). The research of fault diagnosis expert system for the key parts of pure electric truck is carried out. In this paper, the structure and working principle of battery system and permanent magnet synchronous motor (PMSM) are analyzed. Then the common faults of these two components are collected and sorted out. The hierarchical relationship of each fault event is combed by establishing the fault tree, and the redundancy in the design of knowledge base is reduced. Because of the obvious causality between the fault and the fault symptom of the battery system and the permanent magnet synchronous motor, the knowledge base is established by using the production rule representation method in this paper. By comparing and analyzing the advantages and disadvantages of several fuzzy reasoning methods and combining with the actual situation of pure electric truck, the inference engine is constructed by using Bayesian network, and the causality relationship between fault and fault symptom is determined by causality questionnaire. Then the Bayesian network is constructed and the conditional probability corresponding to a fault symptom is determined when some faults occur or not. Because experts are highly subjective in determining conditional probability, the resulting errors may affect the accuracy of diagnostic results. So this paper puts forward a method to realize the decision aid of diagnosis result by data mining of the history diagnosis record, which makes the expert system improve the diagnostic accuracy with the increase of the number of times of use. Finally, a fault diagnosis expert system with B / S structure is designed and implemented in Java language. The expert system can be used only by using browser login, which has great convenience. This paper makes full use of Bayesian reasoning theory and puts forward a new idea for fault diagnosis of key parts of pure electric truck by developing expert system, which is of high economic value. In addition, the application of data mining technology to optimize the fault diagnosis expert system, to a certain extent, solve the problem of expert system knowledge acquisition difficulty. The expert system has strong generality and can be applied to causal uncertain reasoning in other fields.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U472.9;U469.72;TP182

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