ABS故障诊断毕业设计开题报告

毕业设计（论文）材料之二（2） 安徽工程大学机电学院 本科毕业设计(论文)开题报告 题目： 汽车ABS系统智能故障诊断 课 题 类 型： 设计 eq \o\ac(□,√) 实验研究□ 论文□ 学 生 姓 名： 陈首雨 学 号： 3092105224 专 业 班 级： 自动化2092 教 学 单 位： 电气工程学院 指 导 教 师： 田丽 开 题 时 间： 2013.3.10 2013年 3月10日 开题报告 内容 财务内部控制制度的内容财务内部控制制度的内容人员招聘与配置的内容项目成本控制的内容消防安全演练内容 与要求 1、 毕业设计论文内容及研究意义 (1)防抱死制动系统（ABS，Anti-Brake System）是一种汽车主动安全装置，它在制动过程中根据“车辆-路面”状况，采用电子控制方式自动调节车轮的制动力矩来达到防止车轮抱死的目的，增加行车的安全性[1]。针对ABS系统，研究其执行器和传感器的故障诊断有着重要理论意义及现实意义。（2）神经网络诊断原理在ABS系统执行器和传感器故障诊断中的应用【2】，本文试图从ABS系统执行器和传感器故障诊断的角度研究神经网络诊断的理论问题，即BP神经网络故障诊断原理和方法。（3）利用MATLAB进行仿真来对汽车制动防抱死系统（ABS）进行故障诊断，从而验证神经网络在汽车ABS故障诊断系统中的应用。（只写了研究内容没有写研究意义），再加一点研究意义 二、毕业设计（论文）研究现状和发展趋势（文献综述） 随着汽车行驶速度提高及道路行车密度的增大，对汽车的行驶安全性的要求越来越高，汽车防抱死制动系统ABS是一种在汽车上日益普及的主动安全装置。它通过轮速传感器检测车轮轮速，经过信号处理后的轮速传输至计算机，计算机根据轮速以一定的算法和控制方法来控制电磁阀增减制动压力，防止车轮抱死。ABS能避免汽车制动过程中的侧滑、跑偏、甩尾和丧失转向操纵能力[3]，提高汽车的操纵性和稳定性，缩短制动距离；还能避免轮胎的局部磨损，提高轮胎的使用寿命，具有一定的经济价值。 普通制动系统在湿滑路面上制动，或在紧急制动的时候，车轮容易因制动力超过轮胎与地面的摩擦力而完全抱死。而ABS是常规刹车装置基础上的改进型技术，可分机械式和电子式两种。它既有普通制动系统的制动功能，又能防止车轮锁死，使汽车在制动状态下仍能转向，保证汽车的制动方向稳定性，防止产生侧滑和跑偏，是目前汽车上最先进、制动效果最佳的制动装置。 由于人们对汽车驾驶安全性要求的不断提高以及ABS系统在汽车中的普及，通过对ABS系统故障诊断技术的研究，及时有效的判断其状态，使其长期、安全可靠的运行，对于提高汽车制动系统的可靠性具有十分重要的意义。而目前ABS系统的自诊断系统只能对于断路、短路一些电气故障进行电气检测，当ECU检测到故障时，立即停止ABS功能，并将故障信息以故障码的形式存入到存储器中。如果对故障进行维修后，不及时清除存储器中的故障码，很有可能造成新的故障码与旧的混杂，造成误诊断。因此对于ABS系统智能故障诊断技术的进一步研究是非常必要的。 三、毕业设计（论文）研究方案及工作计划（含工作重点与难点及拟采用的途径） 设计的重点与难点： 1应用汽车整车运动的力学模型，分析制动过程中的运动情况 2利用MATLAB-SIMULINK对整车系统进行建模，并建立ABS执行器和传感器发生故障时的故障模式，采集故障数据，应用BP神经网络进行泛化，从而进行故障诊断。 拟采用的途径： 1.调研，查阅相关资料，搜集样本数据； 2.确定神经网络的输入和输出向量； 3.抽取部分样本数据作为训练样本，利用BP神经网络进行训练； 4.将剩余的样本数据作为检验样本，用上述训练好的神经网络分别进行仿真检验，通过对诊断结果与实际故障类型的比较、分析，找出故障诊断准确率相对最高的神经网络。 具体流程如下： 设计（论文）进度计划 起止日期 （日/月） 内 容 进 程 2/28-3/9 3/9-3/15 3/16-3/22 3/23-3/29 3/30-4/5 4/6-4/12 4/13-4/19 4/20-4/26 4/27-5/3 5/4-5/10 5/11-5/17 5/18-5/24 5/25-5/31 6/1-6/7 6/8-6/14 6/15-6/21 与导师联系,获得课题,写开题报告 搜索相关资料（包括图书馆和网上检索） 整理、消化资料，理清思路 写读书报告 对BP神经网络故障诊断和汽车ABS系统基本了解 研究当执行器和传感器发生故障时会产生什么样的现象 建立汽车ABS系统故障诊断模型，并进行仿真 对神经网络原理和方法学习和消化 建立基于BP神经网络的汽车ABS智能故障诊断的设计方案 对方案进行初步拟稿 最终设计确定 利用MATLAB进行仿真 对调试结果进行分析 评价 LEC评价法下载LEC评价法下载评价量规免费下载学院评价表文档下载学院评价表文档下载 得出预期的结论 撰写毕业论文 检查并修改毕业设计 最终定稿 准备答辩 四、主要参考文献（不少于10篇，期刊类文献不少于7篇，应有一定数量的外文文献，至少附一篇引用的外文文献（3个页面以上）及其译文） [1] 周志立，徐斌. 汽车ABS原理与结构[M]. 机械工业出版社, 2004.110~112 [2] 陈丙珍.人工神经网络在过程工业中的应用[J].中国有色金属学报（工学版），2004.5，14(1)：106~111. [3] 陈朝阳，张代胜，任佩红.汽车故障诊断专家系统的现状与发展趋势[J].机械工程学报，2003.11，39(11)：1~6. [4] 王耀南，孙炜 智能控制理论及应用[J].机械工业出版社，2011.7，48~69. [5] 王海英，袁丽英 吴勃 控制系统的MATLAB仿真与设计，高等教育出版,2011.8 122~199 [6] 王仲生.智能故障诊断与容错控制[M].西北工业大学出版社,2005.4， 240~250. [7] 肖永清,杨忠敏.汽车制动系统的使用与维修[M].中国电力出版社,2004，312~354 [8] 董长虹. 神经网络与应用.北京：国防工业出版社，2005，103-105 [9] 陈丙珍.人工神经网络在过程工业中的应用[J].中国有色金属学报（工学版），2004.5，14(1)：106~111 [10] Henrik NiemannFault.Tolerant Control based on Active Fault Diagnosis,1996,12：95~135. [11] BHARITKAR S,MENDELJM.The hysteretic Hopfield neural network[J].IEEE Trans on Neural Networks,2000,11(4)：897~888 附:参考外文文献及其译文 Soft computing methods in motor fault diagnosis Abstract 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 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 computing 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 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 strategies 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 electrical 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, certainty, 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. Therefore, 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. This 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 present 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 3–6, respectively. Their advantages and disadvantages are also briefly reviewed and compared. Some conclusions are finally drawn 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 defective 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 examination. 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 motor, 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 operating 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 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, 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 scheme 3. Neural networks-based motor fault diagnosis Due to their powerful nonlinear 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-propagation (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, ω the rotor speed, 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, ω) and outputs (Nc, Bc) are highly nonlinear. Thus, a BP neural network is applied to approximate this relationship. The training structure is shown in Fig. 3. The values of I and ω 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 ω, e.g. I2 and ω2, which would increase the convergence speed. 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 into 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 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 faulty machines. Their promising scheme has the diagnosis accuracy of 100% in simulations. Fig. 2. BP neural network for incipient fault detection Fig. 3. Training phase for neural network-based motor fault detection Fig. 4. Neural network-based motor fault detection From the discussions above, it is 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 neural networks-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 output 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 fuzzy logic to the diagnosis of induction motor stator and phase conditions. Their diagnosis structure, whose kernel is just a representative fuzzy reasoning system including a fuzzification interface, inference engine, fuzzy rule base, and a defuzzification unit, is illustrated in Fig. 5. The conditions of the stator and phases are represented with three rectangular membership functions, i.e. good, damaged, and seriously damaged. Totally, there are 12 heuristic IF–THEN fuzzy inference rules applied to detect the two aforementioned faults, for instance 1. IF Ib is small THEN the stator is damaged. 2. 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 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 ability 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 fusion of neural networks and fuzzy logic–neural-fuzzy technique. 5. Motor fault diagnosis using neural-fuzzy technique As we know, both neural networks 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 capability. 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 neural networks and fuzzy logic into a hybrid system–neural-fuzzy, so that both of them can overcome their individual drawbacks as well as benefit from each other’s merits. In fact, neural-fuzzy technique has found many promising applications in the field of motor fault diagnosis. Although fuzzy neural 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 cannot 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 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 optimization schemes, such as the steepest descent method, 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 methods 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 diagnosis 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 GA in neural network-based motor fault diagnosis Since GA is only an auxiliary optimization method, it cannot be applied independently in practice. The combination of GA with other motor fault diagnosis schemes has demonstrated enhanced performance in global and near-global minimum search. However, optimization 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. Conclusions In this paper, we gave an overview on the recent progresses of soft 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 computing methods can provide us with 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 two categories of methodologies, soft computing and hard computing. This overview paper is the starting point for our future research activities in the field of soft computing-based fault diagnosis of electric motors. Acknowledgements The authors would like to thank the anonymous reviewer for his insightful comments and constructive suggestions that have improved the paper. This research work was funded by the Academy of Finland. 电机故障诊断的软计算方法 摘 要 在过去的十年里，软计算(计算智能)引起了来自不同领域研究的极大兴趣。在本文中，我们对基于软计算的电机故障诊断这个新兴领域的最近发展事态进行了概述。几个典型故障诊断方案运用生动的图表以及简化算法来利用神经网络、模糊逻辑、神经模糊和遗传算法。他们的优缺点被进行了比较和讨论。我们认为软计算方法有极大的潜力在于处理困难的故障检测和诊断问题。 1. 介绍 交流和直流电机广泛应用在各种工业应用。改变的工作环境和动态加载总是拉紧和磨损电动机而且导致例如短路、轴承破碎和转子条损坏的潜在故障。如果得不到正确的检测与处理，这些错误可能导致严重的性能退化和最终的系统故障。提高安全性和可靠性才能实现良好的早期故障诊断策略，这个策略会让预防性维护保养的概念得以产生。此外, 采用先进的检测方法去寻找那些发展中的失败，使得大量的维护成本得以保留。因此,马达驱动监测、故障检测与诊断在电气工程领域是非常重要的和富有挑战性的课题。 软计算方法作为一个新兴的智能计算,匹敌人类头脑推理和学习不确定性和不精确情况的卓越能力。与硬计算方法相比之下, 只有处理精度、确定性、和严密性,它在获取不精确或次优是有效的,但对经济而有竞争力的现实世界问题的解决方案就不是有效的了。正如我们所知, 来自运营商的定性信息在早期阶段的准确和鲁棒电机故障诊断中起重要作用。因此,对于这个区域的软计算介绍,可以给我们提供比常规方案更具特色的适应性、灵活性、和嵌入式语言知识。一个关于电机故障检测与诊断方法的最新报告最近刊登在一个特殊的章节。 这篇综述如下组织。首先,我们给第二节的传统的电机故障诊断做个简要介绍。包括工作原理、系统构成、和计算算法的基于软计算方法会在接下来的章节讨论。我们分别在3 - 6节提出一些有趣的利用软计算方法的电机故障诊断方案,如神经网络、模糊逻辑、模糊,遗传算法（GAs）。简要介绍和比较了它们的优点和缺点。一些结论被写在文章的结尾。 2.传统的电机故障诊断方法 有众多电机故障检测与诊断的传统方法。最简单的方法就是直接检查。它需要仔细检查个体电机的情况来找到有问题的故障。如果电机有一个装有润滑油的齿轮箱，一个类似的程序就被称为电机被润滑的粒子分析。首先采样石油,然后石油被 实验室 17025实验室iso17025实验室认可实验室检查项目微生物实验室标识重点实验室计划 检查,来检测可能的缺点。不过,这会导致费时和昂贵的检查。另一方面,上述两种方法更适用进行日常的维护保养。 经典的参数估计方法也可以合理地应用于电机故障检测与诊断问题。潜在的想法是基于一些实际电机的测量信号,我们用参数识别技术来估计有关资料电机的工作状态。图1说明了这种故障检测过程。参数估计策略适合实时的案例。然而,它需要深刻理解电机的工作原理以及一个精确的数学模型。另外,随着电机的老龄化，原模型变得不是那么准确了。 在过去的几年，利用软计算克服上述传统诊断策略面临的困难。一般来说,软计算方法包括三个基本研究范式:神经网络、模糊逻辑、气体(进化计算)。在本文，我们讨论了最近基于软计算电机故障诊断研究的进展。,在这个电机故障检测与诊断区域，神经网络、模糊逻辑、和它们融合在一起的GAs,如神经模糊这些应用将会分别出现在接下来的段落里。 图1.利用参数估计策划的电机故障诊断 3.基于神经网络的电机故障诊断 由于其强大的非线性函数近似法和自适应学习能力,神经网络在电机故障诊断领域已经引起了广泛的关注。Chow和他的同事们已经对基于神经网络的故障检测方案进行了综合调查。他们为初期的电机故障诊断提出了一个典型的BP神经网络结构,正如图2所举的例子。这里的初期故障指的是在一个式笼形异步电动机的匝间绝缘和轴承磨损。在图2, I是定子的稳定电流，ω是转子转速，以及Nc和Bc是电机绕组绝缘和轴承的条件。从异步电动机的特性方程，我们知道在输入(I, ω)和输出(Nc, Bc)之间的关系是高度非线性的。因此,建立BP神经网络应用到近似这种关系。训练结构如图3。I和ω的测试值很容易从在线测试数据中得到。事实上，图2的BP神经网络的输入可以进一步扩大到包含更多的I和ω, 如I2和ω2，这些将会增加收敛速度。另一方面,Nc以及Bc应该被图3显示的人类专家所评估。更确切地说,基于对 培训 焊锡培训资料ppt免费下载焊接培训教程 ppt 下载特设培训下载班长管理培训下载培训时间表下载 电机的工作条件和定性故障诊断知识的观察，定量描述了电机的Nc和Bc测量值分别被分为三个条件水平,{好,公平的,坏},检测Nc和Bc的消息队列。在神经网络被训练来学习专家诊断经验之后，他如图4所示被应用在网上。从电机运行条件、定子电流、转子转速上判断，神经网路可根据上述三种故障的水平显示故障隐患。Filippetti等提出了一种类似基于BP神经网络的电机故障诊断方案来检测破碎的转子条的数量。神经网络的训练数据从健全以及模拟故障的机器中获得。他们有前途的方案在模拟中有正确率100%的诊断 图2. BP神经网络故障检测的开端 图3.神经网络的电机故障检测的训练阶段 图4.神经网络的电机故障检测 从以上讨论,得出对电机故障诊断利用神经网络的动机是由于它们的适应性和非线性逼近能力,可以设置指示故障和有效的测量信号之间的关系。然而,基于神经网络的电机故障诊断的关键缺点是来自电机操作员的定性和语言信息不能直接利用或镶嵌在神经网络里,因为他们以数值为导向的黑盒测试的结构。此外,它甚至难以解明映射一个受过训练的意义深远的神经网络故障诊断规则的输入和输出。 4.基于模糊逻辑的电机故障诊断 利用明确的故障诊断语言知识, 使用模糊逻辑的众多电机故障诊断方法已经开始了研究。Nejjari和Benbouzid把模糊逻辑应用到诊断异步电动机的定子和相位条件上。他们的诊断结构如图5所示,其核心是只是个包括一个模糊化界面,推理机，模糊规则基础和一个去模糊化单位的典型模糊推理系统。定子和相数的情况以三个矩形隶属函数为代表,即良好,损坏,和严重损坏。总之,有12个启发式if - then模糊推理规则应用于检测两个上述缺点,例如 1. 如果Ib小然后定子损坏。 2. 如果Ic中等然后定子处于好的状态。. 该诊断方法在检测严重的状况精度达到91.7%且在轴承“好”和“坏”的状态下精度达到100%。 基于模糊逻辑的电机故障诊断方法有嵌入式语言知识和近似推理能力的优势。然而, 制度 关于办公室下班关闭电源制度矿山事故隐患举报和奖励制度制度下载人事管理制度doc盘点制度下载 的设计很大程度上靠所得练习操作上获得的直觉经验。模糊隶属度函数和模糊规则不能在任何检测中被保证获得最佳。此外,模糊逻辑系统缺乏自主学习的能力,这是在一些高要求实时故障诊断病例中所强制的。上述两个缺陷可以来用神经网络和模糊逻辑神经模糊技术的融合部分克服。 5.利用模糊神经的电机故障诊断 正如我们所