Diesel Fault Diagnosis Technology Based on the theory of Fuzzy Neural Network Information Fusion

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1 Diesel Fault Diagnosis Technology Based on the theory of Fuzzy Neural Network Information Fusion i ongkun a Xiaojiang e Yong Institute of Vibration Engineering, Dalian University of Technology, Dalian, , China l.hongkun@strath.ac.uk mxjiang@dlut.edu.cn heyong74@163.com Abstract-Because of the system dynamics, function and structure complexity, nonlinearity and uncertainty, the diesel engine fault diagnosis is very difficult. Sensor information fusion, concepts and techniques developed for optimal information processing in distributed multi-sensor environments through intelligent integration of multi-sensor data, has gained popularity over the past decade. A new method of fault diagnosis based information fusion has been brought forward. The main objective of this work is to develop a framework using information fusion theory for fault diagnosis. Fuzzy Neural Network (FNN) as the center of information fusion is applied to diesel engine fault diagnosis. easurement data from different type sensors are integrated and fused together based on FNN information fusion theory. Taken 1135 diesel fault diagnosis as an example, the method of fault diagnosis based on FNN information fusion was studied. This method, which is checked through theory and practice, provides a power means for condition monitoring and fault diagnosis for the diesel engine. The method will have great application foreground. Keywords: Fuzzy neural network, Information fusion, ulti-sensor, Fault diagnosis, Diesel engine 1 Introduction Nowadays, the corporation is more extensive complex and intelligent and the system state needs multi-objective condition monitoring and fault diagnosis to assure normal work. For the system dynamics, function and structure complexity, nonlinearity and uncertainty, the information from single sensor can not be satisfied with system requirement in the big type and complex system condition monitoring and fault diagnosis. Technology based on multi-sensor information fusion has been gotten attention and applied by many researchers [1,2]. ulti-sensor information fusion has always been used at different levels of complexity to reduce ambiguity for the sensor data. It plays an important role in many practical applications such as assemble, military command and control for battlefield management, mobile robot navigation, multiple target tracking, and aircraft navigation. Fault diagnosis based on multi-sensor information fusion has gotten considerable attention in recent years and it just is used in diesel engine fault diagnosis. It will have great application on this area. Diesel engine is a very complex machine. Its fault diagnosis is very difficult. Now many practical problems are just depended on worker experiment. It is not accuracy and difficult to find the fault in time. Information fusion theory brings forward a new method for diesel fault diagnosis. The research on this subject is studied and good conclusion can be gotten [3,4]. Information fusion model is very importance to intelligent multi-sensor data fusion. ulti-sensor information fusion has been characterized as fusion at different levels: data level, feature level, decision level [5]. Neural network is one of main method for multi-sensor information fusion. It has been tested as a good tool for the feature level fusion. Nowadays, it has been used very broadly on the information fusion domain. Neural network has parallel treatment network structure and learning ability by itself. But the knowledge denotation form can t be understood easily and it can t use the language knowledge in the expert domain. The fuzzy logic can utilize language knowledge preferably and the expression form can be understood easily. But the fuzzy logic exists weak of self-learning ability and difficulty in utilized number information. The neural

2 network and the fuzzy logic can be combined together, and FNN is formed. FNN can compensate the shortage of every system and has more superiority attributes. FNN is one of evolution future technology and also the hotspot of research [6,7]. This paper constructs the information fusion fault diagnosis center utilizing FNN. Fault diagnosis of diesel engine is as an example of FNN application. It is tested that the method is good for the diesel engine condition monitoring and fault diagnosis. And the method will have great application foreground in the diesel fault diagnosis area. This paper is organized as follows. Section 2 gives the introduction of diesel feature parameter pick-up. Section 3 introduces the structure of FNN information fusion. In section 4, an example of FNN information fusion application is given. Conclusions based on this study are summarized in section 5. 2 Fault Criterion Parameter Fault criterion data is very important to diagnose with accuracy for the system. Diesel engine is very a complex machine. It is very difficult for the fault diagnosis. So the fault criterion data is very important to select for the fault diagnosis. Through many experiments of diesel engine experiment, the author finds that the performance state parameters are very important to estimate the diesel engine performance stand or fall. So it is also very good fault criterion data for diesel engine fault diagnosis. The cylinder combustion process is closely link to its performance [8]. The diesel engine fuel consumption b e, the max combustion cylinder pressure P max, the temperature of exhaust gas T r, temperature of input cooling water and output cooling water are the important parameter of estimation the diesel engine state. Combustion cylinder pressure can estimate the power ability, temperature of exhaust gas can estimate emission ability and the fuel consumption can estimate economy ability of diesel engine. So we can establish the relation between the fault and symptom from these parameters. And the criterion of diesel engine fault diagnosis can be gotten. Utilizing state parameters for diesel engine fault diagnosis is also the research hotspot in the world. relations for an element to a research domain, entirely belongs to or not. The element must belong to one of the two relations, and there is not any other estate. Using a feature function expression C(x) to express the relation, for a set A: C A (x) = 1 x A 1 0 x A For the complex machine fault diagnosis, the uncertain attribute of symbol can be expressed on the state parameters description. The fault diagnosis can be realized through description of the state parameter, such as: very high, very low, gradual change and break, etc. For an example, the gas exhaust temperature is high. But the sentence is fuzzy. The parameter of fault symbol is existence uncertain attribute. It is very difficult to describe using the tradition method such as yes or no, existence or nonexistence. The severity degree of fault occurrence also has fuzzy attribute. Fuzzy subset can be expressed using the continuous subjection function. The conclusion of fault diagnosis can also be described in some degree. This can make the system describe about fault symbol conclusion and diagnosis conclusion apt to the consideration of people for the objectives. 3.1 Fuzzy Input of Networks For the input feature from data of meanings (such as temperature and pressure is very high or low), there are three values: very high, normal, very low. The value can be in the scope [min max]. The normal value is belongs to [a b]([a b] [min max]). The expression of the subjection function in echelon form: 1 x max Very high x b (2) b x max max b x max b max b x max Normal = 1 a x b (3) x min min x a a min 3 Construction of FNN Fusion Center According to sutra fusion theory, there are only two x a Very low min a 1 min x a x min (4)

3 The measurement state parameters are input from the left, then comes into the fuzzy input level. Every input parameter is changed to three subjections. So the number base point for the fuzzy input level is three times of the input number. X is expressed the output vector of fuzzy input level. X = ( x = ( x 11 (1) 12 (2) 13,, x (3) 41,, x (10) (11) = ( ( x1), ( x1), ( x1),, ( x4), ) T (12) ) T ( x4), ( x4)) have been used in signal processing, system identification and control applications. In [11], it was shown that neural network which used radical basis function as its transfer function could give better performance in terms of accuracy and speed. So in this paper we use the RBFNN to train and recognize. The structure of the RBFNN is displayed in the Figure 2. Input evel RBF evel Adaline evel Output evel The fuzzy data is as the input level. Data is input to neural networks for ratiocination. So the fault diagnosis based information fusion can be achieved [9]. The structure of FNN is displayed in Figure 1. X W1 B1 W2 B2 O S Y S T E Input variable X1 X2 X3 X4 Fuzzy input ( x1) N ( x1) ( x1) ( x4) N ( x4) ( x4) atent evel Output evel Figure 1. Fuzzy neural network 3.2 The Algorithm of Neural Networks [10] y 1 y 2 y 8 Output variable Radical basis function neural network (RBFNN) shares the features of Back Propagation neural networks (BPNN) for pattern recognition. They have been extensively used for on-line and off-line non-linear adaptive modeling and control applications. Now, the BPNN is broadly and maturely used in pattern recognition domain. ost fuzzy neural networks use BP neural network to recognize. But there are also some shortages in practical application. Such as learning convergence speed is very slow and it is easy to appear local minimum. The initial data has great effect on the study performance, etc. RBFNN is one of the neuron classifying organs. Its neural center transformation function can form several segments received domain. They can form complex structure decision field. RBFNN is presented with training set of input-output pairs and used as a training algorithm to learn the nolinear mapping from input to output. Thus it essentially carries out an approximation for nolinear mapping from input to the output. RBFNN Figure 2. Structure of RBFNN RBFNN is one of three feedback neural networks, containing input node level, middle nonlinear dealing level and linear output level. The middle nonlinear dealing level adopts radial symmetry neuron function. The mostly common use is the Gaussian function. The equation can be expressed as (5). 1 Ri ( x) = exp( σ x c 2 i i 2 ) i = 1,2,3,,m (5) x - n dimension input vector; c i - the center of neural networks with the same dimension of x; i - stands for the scalar quality value of width; m- number cell of middle level; x-c i - stands for Euclid distance between x and i; c Neural networks output is the linear combination of neuron function. The expression is equation (6). m y ( x) = w R ( x) k = 1,2,3,, p (6) k 1 ik wik -stands for authority value between the No. i of latent level and the No k node of output data p -stands for the numbers of output node. 3.3 Fault Diagnosis ethod i node When we diagnose the diesel engine fault diagnosis using FNN information fusion, we can adopt the following steps. Firstly, we will compute the subjection

4 degree according to different states. According to the experiment, we can get the three values about parameters. So we can get the practical subjection degree according to equation (2),(3)and(4). These will be as input data of FNN. Secondly, we will go along with the FNN training. The subjection degree of the standard sample is input to FNN. The unit matrix is as to the computation output of standard sample. If there are n samples, we define the output unit as n. That is to say, the input units equal to the output units. We can express the standard samples as the following form[ z z,..., z 0,0,...1,...0]. We use [ z i, 1, i,2 i, n ] to express the input subjection degree vector. [ 0,0,...1,...0] express the ideal output vector. The No. i element is 1 and others are zero. So after training and learning, we can get the FNN. It will be as the center of information fusion fault diagnosis. Thirdly, we can compute the subjection degree of samples for recognition. Then the subjection degree is put into the FNN. The output of FNN is to estimate the diesel engine state. If the input vector is expressed as the form [ zd i, 1 can be expressed ], the output of the FNN distinguish the diesel state according to the max value principle of the output vector. We assume the max data of the output vector is, zi,2,..., zi, i, 1 n, zd i 2,..., zd i,, n [ y i, 1, yi, 2,..., y i,n ]. Finally, we can y i, k = max { y, y } i 1 yi, 2,,... y i, k,... i, n So we can get conclusion that the sample for recognition is corresponding to the No. k state. In this way, we can. know the state of diesel engine. It will be more correctly for the diesel engine fault diagnosis than experience judgment. Now, we will give an example of diesel engine fault diagnosis based on FNN information fusion. 4 Application To validate the application of FNN information fusion on fault diagnosis, an experiment has been carried out on 1135 diesel engine. The 1135 diesel engine is four valve, pressure charging, water cooled, direct injection chamber. The main parameter of the diesel is listed in Table 1. Table 1. ain parameter of 1135 diesel engine Cylinder Diameter d (mm) 135 Piston stoke S(mm) 140 Piston swept volume Vs() 2.0 Compression ratio 17.5 Declare power Pe(KW) 14.7 Declare speed n(r/min) 1500 Bosh pumptype A 10 Fuel nozzle Injection starting pressure P jo (Pa) 22 Fuel supply advance angle fs (CA) 15 There are 8 samples of the neural networks feature vector in the system. Data of the experiment is gotten on the circumstance of 1500rpm and 25% load. Table 2 is the expression types of testing fault data. We can find that some expression of fault types is similar. The neural network can t identify if these parameters are the input vector. Using continuous subjection function to express the subjection degree of fuzzy subclass, the input vector is increased. After subjection degree computation, we use FNN as the information center to diagnose. Table 2. Type and criterion of fault A B C D Normal state (F 1 ) N N N N Injection Nozzle Stifled (F 2 ) Intake Gas Temperature (F 3 ) N Fuel exhaust (F 4 ) Fuel supply advance angle increasing (F 5 ) Fuel supply advance angle decreasing (F 6 ) Injection starting pressure increasing (F 7 ) N Cooled water pump close (F 8 ) N N N A: Brake specific fuel consumption (g/kw h); B: Outlet temperature ( ); C: aximum combustion pressure (pa); D: Difference between intake water temperature and outlet water temperature ( );

5 Table 3. Typical fault fuzzy sample A B C D Normal state (F 1 ) (0,1,0) (0,1,0) (0,1,0) (0,1,0) Injection Nozzle Stifled (F 2 ) (1,0,0) (1,0,0) (0,0.5,0.5) (1,0,0) Intake Gas Temperature (F 3 ) (0.882,0.118,0) (1,0,0) (0,0,1) (0,1,0) Fuel exhaust (F 4 ) (1,0,0) (0.375,0625,0) (0,0.5,0.5) (0,1,0) Fuel supply advance angle increasing (F 5 ) (0,0.048,0.952) (0,0,1) (1,0,0) (0,0,1) Fuel supply advance angle decreasing (F 6 ) (1,0,0) (1,0,0) (0,0,1) (1,0,0) Injection starting pressure increasing (F 7 ) (0,0.093,0.907) (0,0.625,0.375) (1,0,0) (0,1,0) Cooled water pump close (F 8 ) (0,1,0) (0,1,0) (0,1,0) (1,0,0) We can find that F 2 and F 6 are very similar from the Table 2. It is very difficult to identify them. The input data has been classified through the subjection degree according to Table 3. It is very easy to distinguish the feature and diagnose through the neural network. So the diesel fault diagnosis based on information fusion can be realized. Table 4. Testing sample of 1135 diesel engine A B C D Table 5. Testing output of neural network F F F F F F F F FNN as the center of information fusion is established through the eight states studying and training. To check the ability of the system, we select three samples to test. Table 4 is the testing sample of 1135 diesel engine. Table 5 is the output of neural network after the samples input to the FNN information fusion center. According to maximum subjection degree, the results of testing data are corresponding to injection nozzle stifled, fuel exhaust, fuel supply advance angle decreasing. This is accord with the reality condition. From the research as mentioned in the above section, the fault diagnosis and classification will be better based on multi-sensor information fusion. Through the multi-sensor information fusion, the covering range is increased, the believing degree is improved and the credibility is amended. The information fusion based on FNN can compensate the information each other. The fixed quantify relationship between symbol and fault can be well established. We can make the better estimation and criterion. This can improve correct recognition ratio of the sample and accuracy the system recognition ability. We cannot determine the system fault just according to single parameter for a complex system. Because disturbance and some unrelated parameters affect system state. Nowadays, the theory of fault diagnosis has extended to multi variable but not for just depending on single sensor. It can avoid system recognition error for single sensor limitation. ulti-sensor monitoring can afford much state information of system. This can contribute to the improvement of system recognition credibility. It is very useful for complex system condition monitoring and fault diagnosis. 5 Conclusion The relation between symbol and fault is established through state parameters condition monitoring. Using FNN as the center of information fusion, the construction is established for the complex machine fault diagnosis based on information fusion theory. The system can decrease the repository capacity and searching time for

6 computation. We can rapidly diagnose the fault component and its type. ulti-sensor information fusion based on FNN affords a new method for complex machine and improves the accuracy system recognition ratio. The method will have great application foreground. Reference [1] F. Akbaryan, P.R. Bishnoi. Fault diagnosis of multivariate systems using pattern recognition and multisensor data analysis technique, Computers and Chemical Engineering, Vol 25, pp ,2001. [2] Qiu Jing etc. Condition onitoring and ulti-sensor Information Fusion for anufacturing System, China echanical Engineering, Vol 7, No.1, pp.18-20, [3] Sharkey, Amanda J.C.Chandroth Gopoinath O.Sharkey, Nol E. Acoustic emission, cylinder pressure and vibration: A multisensor approach to robust fault diagnosis, Proceedings of the International Joint Conference on Neural Networks 2000, pp [4] Wang Jiangping. Fault Diagnosis Technology Based on Neural Network ultisensor Data Fusion, Journal of echanical Science and Technology Vol 21, No. 1, pp , [5] Belur V.Dasarathy, Sensor Fusion Potential Exploitation-Innovative Architectures and Illustrative Applications, Proceeding of the IEEE Vol 1,pp ,1997. [6] Du Qingling, ao Xiuqing, iu aibo etc. Fault odel Identification of Rotating achines Based on Fuzzy Back Propagation, Transactions of the Chinese Society of Agriculture achinery, Vol 32, No. 4, pp , [7] Weng Shilie, Wang Yonghong. Intelligent Fault Diagnosis of Gas Turbine Based on Thermal Parameter, Journal of Shanghai Jiaotong University Vol 36, No. 2, pp , [8] i ongkun, Xu Feng, uang Yiquan. The Research of Strengthening Combustion on 1135 DI Engine, Proceedings of 8 th Science, Thermal Physics Engineering Federation, Sept. 2000, pp [8] Xu Yunfei, Jia inping, Zhong Binglin etc. Study on Fuzzy Neural Netwok for Fault Diagnosis of Rotating achinery, Journal of Vibration Engineering Vol 9, No. 3, pp , [10] Du aiping, Zhang iang, Shi Xizhi. Identification of Internal Combustion Engine Cylinder Pressure Based on Radial Basis Function Neural Network, Transactions pf Chinese Society for Internal Combustion Engines,Vol 19, No. 3, ,2001. [11] Wang Zhipeng, a Xiaojiang. Fault diagnosis method of rotary machinery based on RBF networks, Journal of Dalian University of Technology, Vol 41, No.6, pp , 2001.