起重机外文翻译-起重机液压系统支腿的智能故障诊断研究【中文4600字】【中英文WORD】
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起重机外文翻译-起重机液压系统支腿的智能故障诊断研究【中文4600字】【中英文WORD】,中文4600字,中英文WORD,起重机,外文,翻译,液压,系统,智能,故障诊断,研究,中文,4600,中英文,WORD
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附件2：外文原文Research of Intelligent Fault Diagnosis for Hydraulic System of Cranes OutriggersLi Wanli*, Hua Jiakui, Wang Pengchen, Zhu Fumin 2 1 .College of Mechanical Engineering, Ton自i University, Shanghai 201804, China2. Logistics Engineering College, Shanghai Maritime University, Shanghai 200135, China *Corresponding Author; Abstract 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.Keywords 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 outriggers1-Temperature sensor; 2-Level sensor; 3-Pressure/flow sensor; 4-Pressure sensorThe 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 temperatureWhen the leg system works properly, the oil temperature is usually 4060 .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 tankWhen 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 systems hydraulic oil level will change according to the condition of work.(3) The oil pressure on gear pump exitHydraulic 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 pumpIf 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 cylinderWhen 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 system2.2 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 tothe 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 oilFig. 4 Membership functions of the oil levelFig. 5 Membership functions of the oil relief pressureFig. 6 Membership functions of the work pressure of hydrocylinderFig. 7 Membership functions of the work flow of hydrocylinderFig. 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 originalprecise input into fuzzy membership value between 01.2.3 Training and learning of fuzzy neural network model of the hydraulic systemWe make the characteristic signal of the hydraulic system of outriggers as the fuzzy neural networks input and the corresponding failure causes of system as fuzzy neural networks 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 trainings 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 diagnosisFig. 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 architectureFig. 11 Hardware architectureFig. 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 phenomena, fault location and fault cause.Maintenance suggestion consists of recommendations for machine operation in words or graphs. Tool buttons includes initial diagnosis button, radar view button, stop diagnosis button and exit button. Initial diagnosis button and stop diagnosis button designed by the way of interlock. The radar view button leads to the radar view of failure probability of the diagnosis system. In the process of condition monitoring and fault diagnosis, the probabilities of failures and the probability that each fault affects overall faults is shown in the radar view int (figure 13). In the process of diagnosis, all the probabilities of each failure are saved to the radar data table. When radar is monitoring, the radar data table is displayed and refreshed to the data variations, which is synchronized to the diagnosis result.4 Conclusions This paper presents our design and implementation of the fault diagnos
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