開題報告- 汽車ABS系統(tǒng)智能故障診斷

1、畢業(yè)設(shè)計（論文）材料之二（2）安徽工程大學(xué)機電學(xué)院本科畢業(yè)設(shè)計(論文)開題報告題目： 汽車ABS系統(tǒng)智能故障診斷 課 題 類 型： 設(shè)計 eq oac(,) 實驗研究 論文 學(xué) 生 姓 名： 陳首雨學(xué) 號： 3092105224專 業(yè) 班 級： 自動化2092教 學(xué) 單 位： 電氣工程學(xué)院指 導(dǎo) 教 師： 田麗開 題 時 間： 2013年 3月10日開題報告內(nèi)容與要求畢業(yè)設(shè)計論文內(nèi)容及研究意義(1)防抱死制動系統(tǒng)（ABS，Anti-Brake System）是一種汽車主動安全裝置，它在制動過程中根據(jù)“車輛-路面”狀況，采用電子控制方式自動調(diào)節(jié)車輪的制動力矩來達到防止車輪抱死的目的，增加行車的安

2、全性1。針對ABS系統(tǒng)，研究其執(zhí)行器和傳感器的故障診斷有著重要理論意義及現(xiàn)實意義。（2）神經(jīng)網(wǎng)絡(luò)診斷原理在ABS系統(tǒng)執(zhí)行器和傳感器故障診斷中的應(yīng)用【2】，本文試圖從ABS系統(tǒng)執(zhí)行器和傳感器故障診斷的角度研究神經(jīng)網(wǎng)絡(luò)診斷的理論問題，即BP神經(jīng)網(wǎng)絡(luò)故障診斷原理和方法。（3）利用MATLAB進行仿真來對汽車制動防抱死系統(tǒng)（ABS）進行故障診斷，從而驗證神經(jīng)網(wǎng)絡(luò)在汽車ABS故障診斷系統(tǒng)中的應(yīng)用。（只寫了研究內(nèi)容沒有寫研究意義），再加一點研究意義二、畢業(yè)設(shè)計（論文）研究現(xiàn)狀和發(fā)展趨勢（文獻綜述）隨著汽車行駛速度提高及道路行車密度的增大，對汽車的行駛安全性的要求越來越高，汽車防抱死制動系統(tǒng)ABS是一種在汽

3、車上日益普及的主動安全裝置。它通過輪速傳感器檢測車輪輪速，經(jīng)過信號處理后的輪速傳輸至計算機，計算機根據(jù)輪速以一定的算法和控制方法來控制電磁閥增減制動壓力，防止車輪抱死。ABS能避免汽車制動過程中的側(cè)滑、跑偏、甩尾和喪失轉(zhuǎn)向操縱能力3，提高汽車的操縱性和穩(wěn)定性，縮短制動距離；還能避免輪胎的局部磨損，提高輪胎的使用壽命，具有一定的經(jīng)濟價值。普通制動系統(tǒng)在濕滑路面上制動，或在緊急制動的時候，車輪容易因制動力超過輪胎與地面的摩擦力而完全抱死。而ABS是常規(guī)剎車裝置基礎(chǔ)上的改進型技術(shù)，可分機械式和電子式兩種。它既有普通制動系統(tǒng)的制動功能，又能防止車輪鎖死，使汽車在制動狀態(tài)下仍能轉(zhuǎn)向，保證汽車的制動方向穩(wěn)

4、定性，防止產(chǎn)生側(cè)滑和跑偏，是目前汽車上最先進、制動效果最佳的制動裝置。由于人們對汽車駕駛安全性要求的不斷提高以及ABS系統(tǒng)在汽車中的普及，通過對ABS系統(tǒng)故障診斷技術(shù)的研究，及時有效的判斷其狀態(tài)，使其長期、安全可靠的運行，對于提高汽車制動系統(tǒng)的可靠性具有十分重要的意義。而目前ABS系統(tǒng)的自診斷系統(tǒng)只能對于斷路、短路一些電氣故障進行電氣檢測，當(dāng)ECU檢測到故障時，立即停止ABS功能，并將故障信息以故障碼的形式存入到存儲器中。如果對故障進行維修后，不及時清除存儲器中的故障碼，很有可能造成新的故障碼與舊的混雜，造成誤診斷。因此對于ABS系統(tǒng)智能故障診斷技術(shù)的進一步研究是非常必要的。三、畢業(yè)設(shè)計（論文

5、）研究方案及工作計劃（含工作重點與難點及擬采用的途徑）設(shè)計的重點與難點：1應(yīng)用汽車整車運動的力學(xué)模型，分析制動過程中的運動情況2利用MATLAB-SIMULINK對整車系統(tǒng)進行建模，并建立ABS執(zhí)行器和傳感器發(fā)生故障時的故障模式，采集故障數(shù)據(jù)，應(yīng)用BP神經(jīng)網(wǎng)絡(luò)進行泛化，從而進行故障診斷。擬采用的途徑：1.調(diào)研，查閱相關(guān)資料，搜集樣本數(shù)據(jù)；2.確定神經(jīng)網(wǎng)絡(luò)的輸入和輸出向量；3.抽取部分樣本數(shù)據(jù)作為訓(xùn)練樣本，利用BP神經(jīng)網(wǎng)絡(luò)進行訓(xùn)練；4.將剩余的樣本數(shù)據(jù)作為檢驗樣本，用上述訓(xùn)練好的神經(jīng)網(wǎng)絡(luò)分別進行仿真檢驗，通過對診斷結(jié)果與實際故障類型的比較、分析，找出故障診斷準確率相對最高的神經(jīng)網(wǎng)絡(luò)。具體流程如下

6、： 查閱資料和搜集數(shù)據(jù) 確定神經(jīng)網(wǎng)絡(luò)的輸入和輸出向量收集訓(xùn)練樣本，利用BP神經(jīng)網(wǎng)絡(luò)進行訓(xùn)練收集檢驗樣本，用上述訓(xùn)練好的神經(jīng)網(wǎng)絡(luò)分別進行仿真檢驗驗驗比較仿真結(jié)果，進行分析，找出故障診斷準確率相對最高的神經(jīng)網(wǎng)絡(luò)設(shè)計（論文）進度計劃起止日期（日/月）內(nèi) 容 進 程2/28-3/93/9-3/153/16-3/223/23-3/293/30-4/54/6-4/124/13-4/194/20-4/264/27-5/35/4-5/105/11-5/175/18-5/245/25-5/316/1-6/76/8-6/146/15-6/21與導(dǎo)師聯(lián)系,獲得課題,寫開題報告搜索相關(guān)資料（包括圖書館和網(wǎng)上檢索）整理

7、、消化資料，理清思路寫讀書報告對BP神經(jīng)網(wǎng)絡(luò)故障診斷和汽車ABS系統(tǒng)基本了解研究當(dāng)執(zhí)行器和傳感器發(fā)生故障時會產(chǎn)生什么樣的現(xiàn)象建立汽車ABS系統(tǒng)故障診斷模型，并進行仿真對神經(jīng)網(wǎng)絡(luò)原理和方法學(xué)習(xí)和消化建立基于BP神經(jīng)網(wǎng)絡(luò)的汽車ABS智能故障診斷的設(shè)計方案對方案進行初步擬稿最終設(shè)計確定利用MATLAB進行仿真對調(diào)試結(jié)果進行分析評價得出預(yù)期的結(jié)論撰寫畢業(yè)論文檢查并修改畢業(yè)設(shè)計最終定稿準備答辯四、主要參考文獻（不少于10篇，期刊類文獻不少于7篇，應(yīng)有一定數(shù)量的外文文獻，至少附一篇引用的外文文獻（3個頁面以上）及其譯文）1 周志立，徐斌. 汽車ABS原理與結(jié)構(gòu)M. 機械工業(yè)出版社, 2004.110112

8、2 陳丙珍.人工神經(jīng)網(wǎng)絡(luò)在過程工業(yè)中的應(yīng)用J.中國有色金屬學(xué)報（工學(xué)版），2004.5，14(1)：106111.3 陳朝陽，張代勝，任佩紅.汽車故障診斷專家系統(tǒng)的現(xiàn)狀與發(fā)展趨勢J.機械工程學(xué)報，2003.11，39(11)：16.4 王耀南，孫煒 智能控制理論及應(yīng)用J.機械工業(yè)出版社，2011.7，4869.5 王海英，袁麗英 吳勃 控制系統(tǒng)的MATLAB仿真與設(shè)計，高等教育出版,2011.8 1221996 王仲生.智能故障診斷與容錯控制M.西北工業(yè)大學(xué)出版社,2005.4， 240250.7 肖永清,楊忠敏.汽車制動系統(tǒng)的使用與維修M.中國電力出版社,2004，3123548 董長虹.

9、神經(jīng)網(wǎng)絡(luò)與應(yīng)用.北京：國防工業(yè)出版社，2005，103-1059 陳丙珍.人工神經(jīng)網(wǎng)絡(luò)在過程工業(yè)中的應(yīng)用J.中國有色金屬學(xué)報（工學(xué)版），2004.5，14(1)：10611110 Henrik NiemannFault.Tolerant Control based on Active Fault Diagnosis,1996,12：95135.11 BHARITKAR S,MENDELJM.The hysteretic Hopfield neural networkJ.IEEE Trans on Neural Networks,2000,11(4)：897888附:參考外文文獻及其譯文Soft

10、 computing methods in motor fault diagnosisAbstract During the last decade, soft computing (computational intelligence) has attracted great interest from different areas of research. In this paper, we give an overview on the recent developments in the emerging field of soft computing-based electric

11、motor fault diagnosis. Several typical fault diagnosis schemes using neural networks, fuzzy logic, neural-fuzzy, and genetic algorithms, with descriptive diagrams as well as simplified algorithms are presented. Their advantages and disadvantages are compared and discussed. We conclude that soft comp

12、uting methods have great potential in dealing with difficult fault detection and diagnosis problems. 1. Introduction The ac and dc motors are intensively applied in various industrial applications . Changing working environment and dynamical loading always strain and wear motors and cause incipient

13、faults such as shorted turns, broken bearings, and damaged rotor bars .These faults can result in serious performance degradation and eventual system failures, if they are not properly detected and handled. Improved safety and reliability can be achieved with appropriate early fault diagnosis strate

14、gies leading to the concept of preventive maintenance. Furthermore, great maintenance costs are saved by applying advanced detection methods to find those developing failures. Motor drive monitoring, fault detection and diagnosis are, therefore, very important and challenging topics in the electrica

15、l engineering field.Soft computing is considered as an emerging approach to intelligent computing, which parallels the remarkable ability of the human mind to reason and learn in circumstances with uncertainty and imprecision. In contrast with hard computing methods that only deal with precision, ce

16、rtainty, and rigor, it is effective in acquiring imprecise or sub-optimal, but economical and competitive solutions to real-world problems. As we know, qualitative information from practicing operators may play an important role in accurate and robust diagnosis of motor faults at early stages. There

17、fore, introduction of soft computing to this area can provide us with the unique features of adaptation, flexibility，and embedded linguistic knowledge over conventional schemes . An up-to-date presentation of motor fault detection and diagnosis methods was recently published on a special section. Th

18、is overview is organized as follows. First, we give a concise introduction to the conventional motor fault diagnosis in Section 2. Soft computing-based approaches, including operating principles, system structures, and computational algorithms, are then discussed in the following sections. We presen

19、t a few interesting motor fault diagnosis schemes using soft computing methods, such as neural networks, fuzzy logic, neural-fuzzy, and genetic algorithms (GAs) in Sections 36, respectively. Their advantages and disadvantages are also briefly reviewed and compared. Some conclusions are finally drawn

20、 at end of the paper.2. Conventional motor fault diagnosis methods There exist numerous conventional approaches for motor fault detection and diagnosis. The most straightforward method is the direct inspection. It requires careful check-over of the condition of individual motor components to find de

21、fective faults. A similar procedure is named particle analysis of lubricate oil of the motor, if the motor has a gear box with oil lubrication. The oil is first sampled and then taken for laboratory check, which detects the possible faults. This will, however, result in a time consuming and costly e

22、xamination. The above two approaches are more suitable, on the other hand, for routine maintenance. Classical parameter estimation methods can also be reasonably applied for motor fault detection and diagnosis problems. The underlying idea is that based on some measurement signals from the actual mo

23、tor, we use parameter identification techniques to estimate relevant information of the motor working condition. Fig. 1 illustrates this kind of fault detection process. The parameter estimation strategy is well-suited for real-time cases. Nevertheless, it requires a deep understanding of the operat

24、ing principle of the motor as well as an accurate mathematical model. In addition, with the aging of the motor, the original model becomes less accurate. During the past few years, soft computing has been employed to overcome the aforementioned difficulties that conventional diagnosis strategies are

25、 facing. In general, soft computing methods consist of three essential paradigms: neural networks, fuzzy logic, and GAs (evolutionary computation) .In our paper, we discuss the recent progresses of soft computing methods-based motor fault diagnosis. The applications of neural networks, fuzzy logic,

26、and GAs together with their fusion, e.g. neuron-fuzzy, in this motor fault detection and diagnosis area will be presented in the following sections, respectively.Fig. 1. Motor fault diagnosis using parameter estimation scheme3. Neural networks-based motor fault diagnosisDue to their powerful nonline

27、ar function approximation and adaptive learning capabilities, neural networks have drawn great attention in the motor fault diagnosis field. Chow and his colleagues have carried out comprehensive investigation on various neural networks-based fault detection schemes .They proposed a typical Back-pro

28、pagation (BP) neural network structure for incipient motor faults diagnosis, as illustrated in Fig. 2 . The incipient faults here refer to the turn-to-turn insulation and bearing wear in a split-phase squirrel-cage induction motor. In Fig. 2, I is the steady-state current of the stator, the rotor sp

29、eed, and Nc and Bc are the conditions of the motor winding insulation and bearing. From the characteristic equations of an induction motor, we know that the relationships between inputs (I, ) and outputs (Nc, Bc) are highly nonlinear. Thus, a BP neural network is applied to approximate this relation

30、ship. The training structure is shown in Fig. 3. The values of I and can be obtained easily from the on-line measurement data. In fact, the inputs of the BP neural network in Fig. 2 could be further expanded to include higher orders of I and , e.g. I2 and 2, which would increase the convergence spee

31、d. On the other hand, Nc and Bc should be evaluated by a human expert as Fig. 3 shows. More precisely, based on the observation of the working condition and qualitative fault diagnosis knowledge of a training motor, the values of Nc and Bc, which quantitatively describe the motor, are classified int

32、o three condition levels, good, fair，bad, to yield Nc and Bc, respectively. After the neural network has been trained to learn diagnosis experience from the expert, it is employed on-line as illustrated in Fig.4. Judging from the motor operating condition, stator current and rotor speed, the neural

33、network can indicate incipient faults according to the above three fault levels. Filippetti et al. proposed a similar BP neural network-based motor fault diagnosis scheme to detect the number of broken rotor bars. The training data for the neural network is acquired from healthy as well as simulated

34、 faulty machines. Their promising scheme has the diagnosis accuracy of 100% in simulations.Fig. 2. BP neural network for incipient fault detectionFig. 3. Training phase for neural network-based motor fault detectionFig. 4. Neural network-based motor fault detection From the discussions above, it is

35、concluded that the motivation of employing neural networks for motor fault diagnosis is due to their self-adaptation and nonlinear approximation abilities, which can set up the relationship between the indication of faults and available measurement signals. However, the critical shortcoming of neura

36、lnetworks-based motor fault diagnosis is that qualitative and linguistic information from the operator of motors cannot be directly utilized or embedded in the neural networks because of their numerically oriented black-box structures. Additionally, it is even difficult to interpret the input and ou

37、tput mapping of a trained neural network into meaningful fault diagnosis rules.4. Fuzzy logic-based motor fault diagnosis To take advantage of linguistic fault diagnosis knowledge explicitly, numerous motor fault diagnosis methods using fuzzy logic have been studied. Nejjari and Benbouzid applied fu

38、zzy logic to the diagnosis of induction motor stator and phaseconditions. Their diagnosis structure, whose kernel is just a representative fuzzy reasoning system including a fuzzification interface, inference engine, fuzzy rulebase, and a defuzzification unit, is illustrated in Fig. 5. The condition

39、s of the stator and phases are represented with three rectangular membership functions, i.e. good, damaged, and seriously damaged. Totally, there are 12 heuristic IFTHEN fuzzy inference rules applied to detect the two aforementioned faults, for instance1. IF Ib is small THEN the stator is damaged.2.

40、 IF Ic is medium THEN the stator is in good condition. This diagnosis approach achieves 91.7% accuracy in detecting severe conditions and 100% accuracy at both good and bad conditions of the bearing. Fuzzy logic-based motor fault diagnosis methods have the advantages of embedded linguistic knowledge

41、 and approximate reasoning capability. However, the design of such a system heavily depends on the intuitive experience acquired from practicing operators. The fuzzy membership functions and fuzzy rules cannot be guaranteed to be optimal in any sense. Furthermore, fuzzy logic systems lack the abilit

42、y of self-learning, which is compulsory in some highly demanding real-time fault diagnosis cases. The above two drawbacks can be partly overcome by the fusionof neural networks and fuzzy logicneural-fuzzy technique.5. Motor fault diagnosis using neural-fuzzy techniqueAs we know, both neural networks

43、 and fuzzy logic have their own advantages and disadvantages. The major drawbacks of BP neural network are its black-box data processing structure and slow convergence speed. On the other hand, fuzzy logic has a similar inference mechanism to the human brain, while it lacks an effective learning cap

44、ability. Auto-tuning the fuzzy rules and membership functions may be difficult in a classical fuzzy logic system. In a word, neural networks are regarded as model free numerical approaches, and fuzzy logic only deals with rules and inference on a linguistic level. Therefore, it is natural to merge n

45、eural networks and fuzzy logic intoa hybrid systemneural-fuzzy, so that both of them can overcome their individual drawbacks as well as benefit from each others merits. In fact, neural-fuzzy technique has found many promising applications in the field of motor fault diagnosis. Although fuzzy neural

46、networks own the advantages from both neural networks and fuzzy logic, most of the existing models, such as ANFIS, cannot deal with fuzzy input/output information directly. A bearing fault diagnosis problem is employed as a test bed for this approach. Simulations demonstrated that their method canno

47、t only successfully detect bearing damages faults but also provide a corresponding linguistic description.6.Genetic algorithms-based motor fault diagnosis A GA is a derivative-free and stochastic optimization method 31. Its orientation comes from ideas borrowed from the natural selection as well as

48、evolutionary process. As a general purpose solution to demanding problems, it has the unique features of parallel search and global optimization. In addition, GA needs less prior information about the problems to be solved than the conventional optimizationschemes, such as the steepest descent metho

49、d, which often require the derivative of objective functions. Hence, it is attractive to employ a GA to optimize the parameters and structures of neural networks and fuzzy logic systems instead of using the BP learning algorithm alone. In principle, the training of all the motor fault diagnosis meth

50、ods discussed above can be implemented using GAs. For instance, Vas introduced GA into the parameter estimation of an induction motor.Betta et al. discussed the use of GA to optimize a neural network-based induction motor fault diagnosis scheme, which is conceptually illustrated in Fig. 5. The diagn

51、osis performance is encouraging: the percentage of correct single-fault detection is higher than 98%. Moreover, it can also cope with double-fault, with correct diagnosis of both faults in about 66% of the considered cases and of at least one fault in about 100% of the cases.Fig. 5. Application of G

52、A in neural network-based motor fault diagnosisSince GA is only an auxiliary optimization method, it cannot be applied independently in practice. The combination of GA with other motor fault diagnosisschemes has demonstrated enhanced performance in global and near-global minimum search. However, opt

53、imization with GA often evolves heavy computation, and is therefore quite time-consuming. Targeted at real-time fault diagnosis, fast GAs with parallel implementation to improve the convergence speed have to be developed.7. ConclusionsIn this paper, we gave an overview on the recent progresses of so

54、ft computing methods-based motor fault diagnosis systems. Several motor fault diagnosis.techniques using neural networks, fuzzy logic, neural-fuzzy, and GAs were concisely summarized. Their advantages and drawbacks were discussed as well. Based on our observations, we conclude that emerging soft com

55、puting methods can provide uswith improved solutions over classical strategies to challenging motor fault diagnosis problems. However, they are not supposed to compete with conventional methods. Instead, more accurate and robust diagnosis approaches should be developed based on the fusion of these t

56、wo categories of methodologies, soft computing and hard computing. This overview paper is the starting point for our future research activitiesin the field of soft computing-based fault diagnosis of electric motors. Acknowledgements The authors would like to thank the anonymous reviewer for his insi

57、ghtful comments and constructive suggestions that have improved the paper. This researchwork was funded by the Academy of Finland.電機故障診斷的軟計算方法摘 要在過去的十年里，軟計算(計算智能)引起了來自不同領(lǐng)域研究的極大興趣。在本文中，我們對基于軟計算的電機故障診斷這個新興領(lǐng)域的最近發(fā)展事態(tài)進行了概述。幾個典型故障診斷方案運用生動的圖表以及簡化算法來利用神經(jīng)網(wǎng)絡(luò)、模糊邏輯、神經(jīng)模糊和遺傳算法。他們的優(yōu)缺點被進行了比較和討論。我們認為軟計算方法有極大的潛力在于處理困

58、難的故障檢測和診斷問題。介紹 交流和直流電機廣泛應(yīng)用在各種工業(yè)應(yīng)用。改變的工作環(huán)境和動態(tài)加載總是拉緊和磨損電動機而且導(dǎo)致例如短路、軸承破碎和轉(zhuǎn)子條損壞的潛在故障。如果得不到正確的檢測與處理，這些錯誤可能導(dǎo)致嚴重的性能退化和最終的系統(tǒng)故障。提高安全性和可靠性才能實現(xiàn)良好的早期故障診斷策略，這個策略會讓預(yù)防性維護保養(yǎng)的概念得以產(chǎn)生。此外, 采用先進的檢測方法去尋找那些發(fā)展中的失敗，使得大量的維護成本得以保留。因此,馬達驅(qū)動監(jiān)測、故障檢測與診斷在電氣工程領(lǐng)域是非常重要的和富有挑戰(zhàn)性的課題。軟計算方法作為一個新興的智能計算,匹敵人類頭腦推理和學(xué)習(xí)不確定性和不精確情況的卓越能力。與硬計算方法相比之下,

59、只有處理精度、確定性、和嚴密性,它在獲取不精確或次優(yōu)是有效的,但對經(jīng)濟而有競爭力的現(xiàn)實世界問題的解決方案就不是有效的了。正如我們所知, 來自運營商的定性信息在早期階段的準確和魯棒電機故障診斷中起重要作用。因此,對于這個區(qū)域的軟計算介紹,可以給我們提供比常規(guī)方案更具特色的適應(yīng)性、靈活性、和嵌入式語言知識。一個關(guān)于電機故障檢測與診斷方法的最新報告最近刊登在一個特殊的章節(jié)。這篇綜述如下組織。首先,我們給第二節(jié)的傳統(tǒng)的電機故障診斷做個簡要介紹。包括工作原理、系統(tǒng)構(gòu)成、和計算算法的基于軟計算方法會在接下來的章節(jié)討論。我們分別在3 - 6節(jié)提出一些有趣的利用軟計算方法的電機故障診斷方案,如神經(jīng)網(wǎng)絡(luò)、模糊邏

60、輯、模糊,遺傳算法（GAs）。簡要介紹和比較了它們的優(yōu)點和缺點。一些結(jié)論被寫在文章的結(jié)尾。2.傳統(tǒng)的電機故障診斷方法有眾多電機故障檢測與診斷的傳統(tǒng)方法。最簡單的方法就是直接檢查。它需要仔細檢查個體電機的情況來找到有問題的故障。如果電機有一個裝有潤滑油的齒輪箱，一個類似的程序就被稱為電機被潤滑的粒子分析。首先采樣石油,然后石油被實驗室檢查,來檢測可能的缺點。不過,這會導(dǎo)致費時和昂貴的檢查。另一方面,上述兩種方法更適用進行日常的維護保養(yǎng)。 經(jīng)典的參數(shù)估計方法也可以合理地應(yīng)用于電機故障檢測與診斷問題。潛在的想法是基于一些實際電機的測量信號,我們用參數(shù)識別技術(shù)來估計有關(guān)資料電機的工作狀態(tài)。圖1說明了這