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汽车起重机液压系统的设计与计算 外文翻译

汽车起重机液压系统的设计与计算  外文翻译
汽车起重机液压系统的设计与计算  外文翻译

毕业设计(论文)外文资料翻译

学院:机械工程学院

专业:机械设计制造及其自动化

姓名:尤兴亚

学号: 0902070438 外文出处:Corresponding Author

cnlwl@https://www.doczj.com/doc/dd18155682.html,

附件: 1.外文资料翻译译文;2.外文原文。

起重机液压系统支腿的智能故障诊断研究

李万里, 黄佳奎, 王鹏晨, 朱福明

机械工程学院,同济大学,上海201804,中国;物流工程学院,上海海事大学,上海200135,中国;

摘要:随着起重机液压系统越来越复杂,要求故障诊断更加快速和全面。根据起重机支腿液压系统的结构特点,本文提出了一种快速而广泛的硬件和软件体系结构模型的空调监测与故障诊断系统。在本文中,树的诊断方法和模糊神经网络理论的应用为液压系统的故障诊断提供了理论基础以及实现方法。

关键字:起重机,故障诊断,神经网络

1、引言

汽车起重机是一种重要的工程机械。以其日趋复杂的结构和功能,它更倾向于复杂的问题,所以很难诊断起重机支架液压系统的故障。在这样的场景中,一个单一的理论或方法,无论是聪明还是经典的都不足以实现全面、准确、快捷的故障诊断。然而,结合了两个或更多经典和智能的方法,它可能是一个准确、快速的折中诊断方法。本文利用联合诊断算法(模糊神经网络的故障诊断)为支架的起重机液压系统诊断。该算法实现了硬件平台和软件模型的联合诊断,实现了状态监测和故障诊断液压系统。

2、建立基于模糊神经网络稳定支撑的液压系统的故障诊断模型

模糊神经网络(FNN)结构模型收集有神经网络和模糊理论的优势。用模糊神经网络的故障诊断液压系统的稳定支撑如图1所示。

图1结构模型的模糊神经网络算法

基于模糊神经网络的BP(反向传播)神经网络,采用串联方式与模糊系统。输入和输出的网络是模糊量和成员的一些特性和一些模型。网络结构分为五层,输入层、隐层、模糊层、输出层、模糊化消除层。

输入层是第一层网络。这层接收输入特征信号从外面直接传输之前的特征信号到二层——模糊神经元。转移重量是1。节点的数量在输入层取决于数量的特征信号的诊断。

模糊层是第二层的网络。它的功能是计算成员的输入特性信号,属于模糊集合中的每个变量值,根据隶属函数的模糊子集。模糊化后,每个输入层节点对应于三个模糊层节点,代表高边,正常和低侧分别。因此,节点的数量是3倍数量的输入节点。

隐藏层是第三层网络。它是用于实现从输入变量模糊值映射到输出变量的模糊值。激活函数使用是乙状结肠函数。节点的数量是两倍数量的模糊层节点,根据柯尔莫哥洛夫定理。在培训过程中,调整可以根据不同程度的准确性。

输出层是第四层的网络。每个节点对应的每个故障引起的液压子系统。输出值是会员大小隶属于故障原因。节点的数量对应于多种原因引起的典型故障的液压系

统。模糊化消除层是第五层的网络。阐述了模糊结果输出层和输出明确的诊断结果。清晰的计算是基于最小阈值原则(会员价值的故障组件应大于某一阈值,这是定义在调试。的值应该设置为一个适当的值。一个大型会员价值可能会导致一些错误的无知,而小值可能导致假警报的缺点)。

特征信号的液压系统工况选择如下:油温的液压系统,油位在罐、油的压力和流量泵出口、操作压力和泄油压力的水平和垂直液压缸的每条腿。监测信号的分布如图2所示。

1-温度传感器;2-传感器;3-压力/流量传感器;4-压力传感器图2特征信号的工作状态的起重机液压系统稳定支撑

除了工作状态的特征信号,信号的选择,包括控制信号拉伸/收缩水平/垂直腿拉伸/收缩,联动水平/垂直腿和选择信号的半拉伸/所有段水平腿等。

(1)、油温

当支腿系统正常工作,油的温度通常是40 ~ 60℃。但当故障发生时,液压油温度可能经常波动。这可能是由于泄漏的齿轮泵;泄漏或粘腿的液压缸;泄漏或太高的调整压力安全阀等。

(2)油位油箱

当支腿系统工作正常,油位根据一定的方式不断变化的。当在系统中遇到的一些液压元件的泄漏或其他故障时,系统的液压油位会根据工作条件改变。

(3)齿轮泵的出口压力油

液压油压力泵的出口是由外部负载。如果在系统遇到故障时,出口油压力会发生剧烈的变化。

(4)输出石油流动的齿轮泵

如果液压泵遇到故障,将在很长一段时间内无法达到一个稳定的范围内输出油流。

(5)水平杆的液压缸的油压

当支腿水平方向伸展,油压杆少液压缸取决于电阻的腿。阻力包括摩擦阻力和液压阻力杆少液压缸。当腿拉开水平,油压杆少液压缸取决于石油缓解压力的方式回油。当腿水平方向伸展,如果泄漏发生在水平液压缸或换向阀控制气缸,油压力杆少液压缸将低于正常范围。因此,腿不能移动或移动缓慢。当腿拉开水平,如果梗阻发生背压值或油过滤器、油压力杆少液压缸将超过正常范围。因此,腿不能返回。

(6)水平杆液压缸的油压

当双腿伸出水平,取决于杆液压缸的油压油溢流压力回油方式。当腿抽到回水平,杆液压缸的压力油的电阻取决于的leg.The电阻包括摩擦阻力和杆液压缸的液压阻力。当腿伸出水平,如果阻塞发生背压阀或油过滤器,油压力杆液压缸将在上述正常范围。因此,双腿不能移动或移动缓慢。当腿抽到回水平,如果发生泄漏的水平液压缸或换向阀控制气缸,杆液压缸的油的压力将低于在正常范围。因此,腿不能返回。

上面的分析描述信号特征之间的关系,故障现象和故障原因。手术期间,每个特征信号与许多现象和原因的故障而引起的故障现象或每个可能是由大量的信号特征。

正如上面提到的,我们可以诊断故障原因的一些模糊神经网络基于传感器我们已经。失败原因如下:缺乏石油、水力泵衰竭,安全阀失效,电磁换向阀故障,双向液压锁故障,泄漏的水平液压缸的腿,泄漏的垂直液压缸的腿,妨碍背压阀和油过滤器等。

根据相关的设计和调谐的液压系统中的参数,特征信号参数的正常范围和严重程度的可能偏差,得到(如表1所示)。

表1正常范围内的液压系统的特征信号

2、2模糊化过程和选择隶属函数的特征信号的液压系统

根据测量的每个特征信号参数的液压系统,我们可以知道如果参数是正常的,偏小或偏大。至于隶属度的范围,即隶属度故障原因和故障现象之间,它被定义为相应的隶属函数。

模糊隶属函数的相关性,实际情况会影响诊断结果直接。因此,确定隶属函数的关键是整个故障诊断。在许多情况下,根据实际情况,最简单有效的方法是使用一些常见的隶属函数来表达一些模糊变量的约。根据过去的经验和实际变化的参数,本文选用常用的贝尔隶属函数作为一个正常状态的隶属函数、隶属度函数下z型是偏小的状态的隶属度函数和了z型隶属函数作为隶属函数偏大的状态。

考虑到没有明显边界的这些模糊概念的偏小,正常和偏小,重叠部分必须设置为这些隶属函数反映在模糊集的隶属函数曲线。选择合适的重叠率是一个重要的因素来保证诊断的可靠性。关于过去的经验,重叠率的隶属函数的文章被选中在0.2和0.6之间。

考虑的数量,形状,位置,分布,重叠率,等后,我们确定信号参数支持的特性(图3,4,5,6,7和8)的隶属度函数的液压系统。

根据实际情况的操作,我们已经调整了参数的隶属函数。

图3隶属函数的液压油的温度图4隶属函数的油位

图5隶属函数的石油释放压力图6隶属函数的工作压力的液压油缸

图7隶属函数的工作流程的液压油缸图8隶属函数的控制油压力

模糊化过程特征信号的参数是传递精确的输入值的特征信号参数模糊隶属度值。首先,输入值的特征信号参数的分布范围,每个范围偏大,偏小,正常传输。其次,进行了模糊化过程的特征信号参数,已被转移到某些域范围。这个过程将使原来的精确的输入到模糊隶属度值介于0 ~ 1。

2.3培训与学习的模糊神经网络模型的液压系统

我们使液压系统的特征信号的稳定支撑作为模糊神经网络的输入和相应的故障原因的系统作为模糊神经网络的输出。培训和学习的过程显示在图9。

每个操作循环包括以下内容:

首先,它读取训练样本的数据和从知识数据库中的各特征参数的范围内,并且

操作输入故障的样本数据的模糊化过程。第二,它写的模糊化过程数据和预期的输出故障样本数据的神经网络。然后计算每层节点的输出由神经网络。第三,计算输出层节点的实际输出与期望输出之间的误差,并确定,如果训练结果满足精度要求。

如果满足精度要求或培训的最大数量,它存储了本次培训的网络权重和阈值转化为知识数据库,前结束训练过程中。

如果精度要求或不符合的最大数目的培训,它实现向后传输计算减少错误的方向上,并调整输出层和隐层的权值和阈值。在那之后,它实现了一个新的正向传输计算来计算的输出错误之前,下一个比较的精度要求和最大的培训。重复的步骤,上述步骤,直到满足两个准则。

3、实施监测与故障诊断

图10显示了状态监测与故障诊断的软件架构。我们采用自上而下的方法,为软件开发。该软件被划分成单独的模块,方便调试,代码维护和扩展。

图11显示硬件架构的监测与故障诊断。由监测传感器,PLC控制系统,数据采集板和车载电脑或PC等硬件设置

图10、软件架构

图11、硬件架构

图12、显示了用户界面的硬件系统的模糊神经网络故障诊断。

用户界面被分为3个区域在视觉上,即实时参数监视区域,诊断报告和维护上的建议示区域和工具按钮区域。

诊断报告包括诊断时间、诊断算法、故障代码、故障ID现象,故障定位和故障原因。维修建议由机器操作的建议在文字或图形。

工具按钮包括初步诊断按钮,雷达视图“按钮,停止诊断按钮和退出按钮。初步诊断按钮和停止的诊断按钮设计联锁的方式。雷达视图“按钮会导致失败的概率诊断系统的雷达视图。

在这个过程的状态监测和故障诊断,故障的可能性和概率,每个断层影响整体故障显示在雷达视图i如(图13)。诊断的过程中,所有的可能性的每个故障都保存到雷达数据表。当雷达监测,雷达数据表显示和刷新的数据变化,这是同步的,诊断结果。

4、结论

介绍了我们设计和实现的故障诊断模型的模糊神经网络的起重机液压系统稳定支撑。一个方法相结合的模糊理论和人工神经网络算法。模型的输入和输出信号,输入信号的范围,选择隶属度函数和模糊化处理是讨论等。实现在软件和硬件平台进行了阐述。本文阐明了理论基础,导致一个实现方法的监测和故障诊断液压系统起重机支架。这个系统也可以用于其他类似的液压系统,如盾构液压系统,液压系统装载机机等。

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附件2:外文原文

R esearch of Intelligent F ault D iagnosis for H ydraulic S ystem of

C ranes O utriggers

Li Wanli'*, Hua Jiakui', Wang Pengchen', Zhu Fumin 2

1 .College of Mechanical Engineering, Ton自i University, Shanghai 201804, China

2.Logistics Engineering College, Shanghai Maritime University, Shanghai 200135, China

*Corresponding Author; cnlwl@https://www.doczj.com/doc/dd18155682.html,

A bstract The complexity of hydraulic systems of crane outriggers is growing, which demands the fault diagnosis of the systems to be faster and comprehensive. Based on the structural characteristics of the hydraulic systems of crane outriggers, this paper proposes a rapid and extensive hardware and software architecture model of conditioning monitoring and fault diagnosis system. In this paper, tree diagnostic method and fuzzy neutral network theory is applied; the theoretical basis as well as the implementation method for this and similar hydraulic systems' fault diagnosis is provided.

K eywords Cranes, Fault diagnosis, Neural network

1 Introduction

Truck crane is an important model of engineering machinery.With its growing complexity in structure and function, it is more prone to complex troubles, so that it is harder to diagnosis the fault for hydraulic system of cranes outriggers. In such scenarios, a single theory or method, whether classic or smart, is far from sufficient to achieve fault diagnosis that are comprehensive, accurate and fast.

Nevertheless, the combination of classic method and smart, two smart methods or more, may make a good compromise between the diagnosis' accuracy and speed. With the above understanding, this paper utilizes a combined diagnosis algorithm, which is the fuzzy neural network, for the fault diagnosis of hydraulic system of crane outriggers. The algorithm is implemented with a hardware platform and a software model of the diagnosis that realized the condition monitoring and fault diagnosis of the hydraulic system.

2 Establishment of fault diagnosis model of the hydraulic system of outriggers based on fuzzy neural network

The fuzzy neural network (FNN) structure model collects the advantages of neural network and fuzzy theory. The FNN used by fault diagnosis of the hydraulic system of outriggers in this paper is shown in figure 1.

The FNN is based on BP (Back Propagation) artificial neural network and uses the tandem way with fuzzy system. The input and output of the network are fuzzy quantity and membership of some features and some models. The network structure is divided into five layers, input layer, fuzzy layer,hidden layer, output layer, fuzzification elimination layer.

Fig. 1 Structure model of FNN algorithm

Input layer is the first layer of the network. This layer receives input characteristic signal from outside before directly transports the characteristic signal to the second floor--fuzzy neurons. The transfer weight is 1. The number of nodes in the input layer depends on the number of characteristic signal of the diagnosis.

Fuzzy layer is the second layer of the network. Its function is to calculate membership of the input characteristics signal that belongs to fuzzy set of each variable value, according to the membership functions of the fuzzy subsets. After fuzzification, each input layer node corresponds to three fuzzy layer nodes, representing the high side, normal and the low side respectively. Therefore, the number of nodes is 3 times of the number of input nodes.

Hidden layer is the third layer of the network. It is used to implement the mapping from input variable fuzzy value to the output variable fuzzy value. The activation function used is Sigmoid function. The number of the nodes is two times of the number of fuzzy layer nodes, according to theorem of Kolmogorov. During the training process, adjustments can be made according to different level of accuracy.

Output layer is the fourth layer of the network. Each node of it is corresponding to each fault causes of the hydraulic subsystem. Output value is the membership size affiliated to the fault causes. The number of the nodes corresponds to the number of causes of typical faults of the hydraulic system. Fuzzification Elimination layer is the fifth layer of the network. It clarifies the fuzzy results of the output layer and outputs the definite diagnosis results. The clarity calculation is based on minimum threshold value principle (Membership value of fault components should be greater than some thresholds, which is defined in debugging. The value should be set to an appropriate value. A large membership value might lead to the ignorance of some faults while a small value may cause false alarms of faults).

Fig. 2 Characteristic signal of work condition of the hydraulic system of cranes outriggers 1-Temperature sensor; 2-Level sensor; 3-Pressure/flow sensor; 4-Pressure sensor

The characteristic signal of the working condition of the hydraulic system is chosen as follows: the oil temperature of the hydraulic system, oil level in tank, oil pressure and flow of pump exit, operating pressure and oil-relief pressure of the horizontal and vertical hydraulic cylinder of each leg. The distribution of monitoring signal is shown in figure 2.

Besides the characteristic signal of the working condition,other signals are chosen, including control signal of stretch/shrinkage of Horizontal/vertical legs, linkage stretch /shrinkage of Horizontal/vertical legs and selection signal of half a stretch/all stretch of Horizontal legs, etc.

(1)The oil temperature

When the leg system works properly, the oil temperature is usually 40~60 ℃.But when fault occurs, the hydraulic oil temperature might experience fluctuations. This might be caused by: leakage of gear pump; leakage or stuck of a leg hydraulic cylinder; leakage or too high adjustment pressure of relief valve, etc.

(2) Oil level in tank

When the leg system works properly, oil level keeps changing according to a certain fashion. When some hydraulic components in the system encounter leakage or other faults,the system's hydraulic oil level will change according to the condition of work.

(3)The oil pressure on gear pump exit

Hydraulic oil pressure on pump exit is determined by external load. If the system encounters faults, the outlet pressure can experience drastic change.

(4) Output oil flow of gear pump

If the hydraulic pump encounters faults, the output oil flow will not be able to reach a stable range

within a long time.

(5)Oil pressure of the horizontal rod-less hydraulic cylinder

When the legs stretched out horizontally, oil pressure of rod-less hydraulic cylinder depends on the resistance of the leg.The resistance includes friction resistance and hydraulic resistance of rod-less hydraulic cylinder. When the legs draw back horizontally, oil pressure of rod-less hydraulic cylinder depends on the oil-relief pressure of back-oil-way. When the legs stretched out horizontally, if leakage happens to the horizontal hydraulic cylinder or the reversing valves that controls the cylinder, the oil pressure of the rod-less hydraulic cylinder will be below the normal range. Consequently, the legs cannot move or move sluggishly. When the legs draw back horizontally, if obstruction happens to the back-pressure value or oil filters, the oil pressure of the rod-less hydraulic cylinder will be above the normal range. Consequently, the legs cannot return.

(6)Oil pressure of the horizontal rod hydraulic cylinder

When the legs stretched out horizontally, oil pressure of rod hydraulic cylinder depends on the oil-relief pressure of back-oil-way. When the legs draw back horizontally, oil pressure of rod hydraulic cylinder depends on the resistance of the leg.The resistance includes friction resistance and hydraulic resistance of rod hydraulic cylinder. When the legs stretched out horizontally, if obstruction happens to back-pressure-valve or oil filters, the oil pressure of the rod-less hydraulic cylinder will be above the normal range. Consequently, the legs cannot move or move sluggishly. When the legs draw back horizontally, if leakage happens to the horizontal hydraulic cylinder or the reversing valves that controls the cylinder, the oil pressure of the rod hydraulic cylinder will be below the normal range. Consequently, the legs cannot return.

The vertical hydraulic cylinder has the similar theory with the horizontal ones.

The above analysis describes the relationship between characteristic signals, fault phenomena and fault causes.During the operation, each characteristic signal is related to many phenomena and causes of fault while each phenomenon or cause of faults may be indicated by many characteristic signals.

As mentioned above, we can diagnosis some failure causes by fuzzy neural network based on the sensor we have. The failure causes are as fellows: shortage of oil, hydraulic pump failure, relief valve failure, electromagnetic reversing valve failure, bi-directional hydraulic lock failure, leakage of horizontal hydraulic cylinder of legs, leakage of vertical hydraulic cylinder of legs, and obstruction of back pressure valve and oil filters, etc.

According to the related design and tuning of the parameters of the hydraulic system, the normal range of characteristic signal parameters and the severity of the possible deviation are obtained (as shown in table 1).

Table 1 Normal range of characteristic signal of the hydraulic system

2.2 Fuzzification process and selection on membership functions of characteristic signal of the hydraulic system

According to the measurement of each characteristic signal parameter of hydraulic system, we can know if the parameter is normal, slants small or slants big. As for the membership degree in the range, namely the membership degree between fault causes and fault phenomena, it is defined by the corresponding membership functions.

The relevance between fuzzy membership functions and actual situation affects the diagnosis results directly. Therefore, to determine the membership function is the key to

the whole fault diagnosis. In many cases, according to the actual situation, the most simple and effective method is to use some common membership function to approximately express some fuzzy variables. According to past experience and actual change of parameters, this paper selects the commonly-used bell membership functions as a normal state of membership functions, the down-Z-type membership functions as slants small state of membership functions and up-Z-type membership functions as slants big state of membership functions.

Considering that there is no obvious boundary of these fuzzy concepts of slants small, normal and slants small, overlapping part must be set for these membership functions reflected in the membership function curve of fuzzy sets. Choosing the right overlap rate is an important factor to guarantee the reliability of the diagnosis. With reference to past experience, the overlap rate of the membership functions of this paper was selected between 0.2 and 0.6.

After a comprehensive consideration of the number, shape,position distribution, overlapping rate and so on, we determined membership functions of characteristic signal parameters of the hydraulic system of outriggers (Figs. 3, 4, 5,6, 7 and 8).

According to actual situation of the operation, we have adjusted the parameters for the membership functions.

Fig. 3 Membership functions of the temperature of hydraulic oil

Fig. 4 Membership functions of the oil level

Fig. 5 Membership functions of the oil relief pressure

Fig. 6 Membership functions of the work pressure of hydrocylinder

Fig. 7 Membership functions of the work flow of hydrocylinder

Fig. 8 Membership functions of the control oil pressure

Fuzzification process of characteristic signal parameters is to transfer the precise input values of characteristic signal parameter to fuzzy membership value. Firstly, the input values of characteristic signal parameter to each range, the range of slants small, slants big, normal are transferred. Secondly, fuzzification process is conducted to the characteristic signal parameters that have been transferred to certain domain range. The process would turn the original precise input into fuzzy membership value between 0~1.

2.3 Training and learning of fuzzy neural network model of the hydraulic system

We make the characteristic signal of the hydraulic system of outriggers as the fuzzy neural network's input and the corresponding failure causes of system as fuzzy neural network's output. Then we set up network model of fault diagnosis respectively as figure 1 .The process of training and learning are shown in fig. 9.

Each operation loop consists the following:

Firstly, it reads training sample data and the range of each feature parameter from the knowledge databases, and operates fuzzification process with the sample data of input fault.Second, it writes the fuzzification process data and the expected output fault samples data into the neural network.Then it calculates the output of each layer node by the neural network. Third, it calculates the error between actual output of output layer node and expected output, and determines if the training results meet the requirement of accuracy.

If precision requirement or to the maximum number of training are met, it stores this training's network weights and threshold into knowledge database, before ends this training process.

If the precision requirement or to the maximum number of trainings are not met, it implements backward transmission calculation on the direction of reducing the error, and adjusts the weights and threshold of output layer and hidden layer. After that, it implements a new forward transmission calculation to calculate the output error before next comparison to the precision requirements and maximum number of trainings. Repeat the steps the above steps until the two criteria are met.

3 Implementation of the monitoring and fault diagnosis

Fig. 10 shows the software architecture of condition monitoring and fault diagnosis.

We adopt the top-down approach for software developing. The software is divided into separate modules, which is convenient for debugging, code maintaining and extensions.

Fig.l l shows the hardware architecture of the monitoring and fault diagnosis. The hardware set consists of monitoring sensor, PLC controller system, data acquisition boards and vehicle-mounted computer or pc, etc.

Fig. 10 Software architecture

Fig. 11 Hardware architecture

Fig. 12 shows the user interface of the hardware system of the fuzzy neural network fault diagnosis.

User interface is divided into 3 areas visually, namely the real-time parameters monitoring area, diagnosis report and maintenance suggestion display area and tools button area.

Diagnosis report consists of diagnosis time, diagnosis algorithm ID, fault code, fault

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外文翻译---港口起重机

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起重机论文中英文资料外文翻译文献

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起重机液压系统设计

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