Abstract: This paper studies the fault diagnosis methods of fuzzy logic and neural networks. Based on the advantages and disadvantages of these two methods, a concatenated method is adopted to combine them. Fuzzy information processing is used to preprocess the input signal, and then the approximation ability of the neural network is used to realize the fault diagnosis. The inference system constructed by this method is applied to the diagnosis of occasional difficult faults in gasoline engines. Software simulation is performed using Matlab. The simulation results show that the method can give high-precision diagnostic results, which is a significant improvement over the method of using fuzzy logic or neural network alone. It has a good identification ability, especially for complex faults of single systems, and has good application prospects. Keywords: fuzzy, preprocessing, neural network, fault diagnosis. [b][align=center]Application of Neuro-net in Fault Diagnosis System for Gasoline Engine LIU Zhao-guang1, PAN Lian1, WANG Jun2 （1.Wuhan University of Science & Technology, Wuhan 430081； 2.Huazhong University of Science and Technology,Wuhan 430074）[/align][/b] Abstract: Research on Fuzzy Theory and Neutral Network Technology in Fault Diagnosis. Based on the advantages and disadvantages of these two methods, this paper combines them with the proposed method. Firstly, a fuzzy method is used to process the information, and fault diagnosis is implemented using the approaching ability of neutral networks. An inference system is constructed to solve complicated faults in gasoline engines using this method. Through simulation in MATLAB, it has been proven that the system can categorize samples well and output with high precision. This represents a significant improvement over purely fuzzy logic methods or neural network methods, especially when the system is highly complex. It has promising application prospects. Keywords: Fuzzy, Measurement, Neural Network, Fault Diagnosis. Neural networks possess powerful nonlinear mapping capabilities, parallel processing capabilities, good learning and fault tolerance, and unique associative memory capabilities. They are widely used in various fields of industrial production, and neural network-based fault diagnosis has become a relatively common fault diagnosis solution. However, because this fault diagnosis system requires a large number of training samples and has a long training time, and the well-trained network connection weights represent knowledge but are insufficient in knowledge acquisition representation, it leads to fragile reasoning. Fuzzy diagnosis determines equipment status by studying the relationship between faults and symptoms. Due to the complexity of actual factors, the relationship between faults and symptoms is difficult to represent with a precise mathematical model. With the emergence of ambiguity in certain fault states, a simple diagnostic result of "whether there is a fault" is no longer sufficient. Instead, it is required to provide the probability of fault occurrence, as well as the location and extent of the fault. Fuzzy fault diagnosis methods utilize fuzzy mathematics to quantify the uncertainty of faults in the system to be diagnosed, and then identify faults based on certain judgment thresholds. The fault diagnosis method based on fuzzy rules first transforms empirical knowledge into fuzzy relations, then uses fuzzy logic to reason about the fuzzy quantities, outputs the fuzzy quantities, and finally converts these fuzzy control quantities into precise outputs to indicate the fault type. Correctly selecting the membership function is the foundation for solving practical problems using fuzzy set theory. The membership function is a quantitative description of a fuzzy concept, and its selection greatly affects the final result. However, there is no unified model for selecting the membership function that accurately reflects the fuzzy concept of the fuzzy set. Fault diagnosis based on fuzzy theory also has some weaknesses: acquiring knowledge for fuzzy diagnosis is difficult; the fuzzy relationship between faults and symptoms is difficult to determine; the system's diagnostic capability relies on a fuzzy knowledge base, has poor learning ability, and is prone to missed and misdiagnosed cases; during fault diagnosis, the reasoning speed is slow, efficiency is low, and capability is weak; and it is difficult to handle the uncertainty in fault diagnosis. Given the characteristics of neural network fault diagnosis and fuzzy fault diagnosis, this paper proposes a fuzzy neural network fault diagnosis method based on fuzzy preprocessing, aiming to combine the advantages of both and overcome their weaknesses. This method is then applied to the diagnosis of occasional and difficult faults in gasoline engines. 1. Fault Diagnosis Methods A fuzzy neural network fault diagnosis system based on fuzzy preprocessing is shown in Figure 1. First, the acquired signals undergo fuzzy preprocessing, and then the neural network is used to diagnose system faults. Most fault diagnosis systems include the system to be diagnosed, signal acquisition, and decision execution system modules shown in Figure 1. The acquired signals are required to reflect the current state of the measured point in real time. The diagnosis system uses the acquired parameters to determine the system's state, providing a basis for the decision execution system. 1.1 Fuzzy Preprocessing of Signals For the acquired numerical signals, their fuzzy membership degrees must first be determined. Fuzzy membership degrees characterize the uncertainty and imprecision of each pattern feature. Correctly determining the membership function is the foundation for solving practical problems using fuzzy set theory. There are many methods to determine the membership function of a fuzzy set. This paper uses the function as the membership function to find the membership degree [4]. The function is defined as follows: λ>0 is the radius of the π function, and c is the center point. From equation (1), it can be seen that the function is a kind of intermediate symmetric membership function. For each input signal f, when the system is working normally, there must be a certain optimal value f[sub]0[/sub], f[sub]max[/sub], f[sub]min[/sub], which represent the maximum and minimum values ​​of f when the system is in various states. The fuzzy subset describing f is {small (L), medium (M), large (B)}. Where: Here, f[sub]d[/sub] is used to control the degree of overlap of adjacent fuzzy sets and thus ensure that for any f, at least one of μ[sub]M[/sub](f), μ[sub]L[/sub](f) and B[sub]M[/sub](f) is greater than 0.5. Therefore, the input signal is fuzzily quantized as: I=[x[sub]1[/sub],x[sub]2[/sub],…x[sub]m-1[/sub],x[sub]m[/sub]] =[μL（f1）, μ[sub]M[/sub]（fn）, μ[sub]B[/sub]（fn）] （6） For some input signals that cannot be expressed numerically, their membership degree can be directly estimated according to the actual situation. It can be set through expert experience, research on the production process, and repeated communication with the factory's technical personnel. 1.2 Neural Network Fault Diagnosis Neural networks are networks that simulate the functions of the biological nervous system (especially the brain). The human brain is composed of approximately 15 billion nerve cells, each of which is connected to thousands or tens of thousands of nerve cells to form a network. Thus, the neural cell model can be viewed as an n-input, single-output information processing unit. The influence of a certain input xi on the neural cell is represented by its influence degree, called the cell's binding weight or efficiency wi. This cell model is shown in Figure 2. The cell's inputs can be strong or weak. When their sum exceeds a certain threshold θ, the cell enters an excited state and produces an output; when their sum is below the threshold, the cell enters an inhibited state and produces no output. This input-output relationship can be written as the possibility of different connection methods between neural cells. Many neural network models have been proposed. Among them, the BP learning algorithm is currently the most widely used and successful learning algorithm in neural network technology. A BP network is a neural network consisting of an input layer, an output layer, and one or more hidden layers. The network learns by being taught by a teacher. Its learning process is to continuously adjust the network weights and thresholds so that the error between the actual output and the expected output gradually decreases until the expected error is reached. For example, a three-layer BP neural network has n inputs, m outputs, and one hidden layer. From the input signal x, the internal units undergo nonlinear transformation to finally obtain the output y. For input x, the expected output is set as yd, while the actual output is y, and the two are generally inconsistent; the input-output function is related to the combination weights of the cells inside the network. The error signal between the expected output and the actual output is: e=y[sub]d[/sub]-y (8) Adjust the combination weights inside the network to reduce the error and improve the network's operation. This is called a learning neural network. The BP algorithm is a nonlinear optimization problem. Currently, this learning method uses the square error of the output as the evaluation function and generally adopts the gradient descent method to correct the combination weights and thresholds of each layer in reverse. The formula for correcting the weight x[sub]ij[/sub] is: (9) In formula (9), w[sub]ji[/sub](n) is the weight of the i to j neurons in the nth step, and the correction value is: (10) ε is the output square error function, and η is the learning rate. The training accuracy of the BP neural network is high, and the generalization result is satisfactory. Moreover, theoretically, a three-layer feedforward neural network can arbitrarily approximate any function with arbitrarily expected accuracy. However, during the learning process, the standard BP algorithm causes the gradient descent adjustment to decrease as the error value decreases, resulting in a long network training time and slow convergence speed; in addition, the network may get trapped in local minima. In view of the above, this paper adopts a three-layer neural network using an improved BP algorithm. The network is shown in the figure. The improvement of the basic BP algorithm in this paper includes two parts: First, a momentum term is added to the adjustment amount of the parameters to be trained, i.e.: Where □w[sub]ji[/sub](n) is the adjustment amount of the basic BP algorithm, α□w[sub]ji[/sub](n-1) is the momentum term, α is the momentum coefficient (generally taken as 0.9), and □w[sub]ji[/sub](n-1) is the weight modification amount of the previous iteration; Second, an adaptive learning rate is adopted. In practical applications, it is difficult to find a learning rate that is optimal from beginning to end. Therefore, it is desirable for the learning rate to be adaptively adjusted according to the error situation. This algorithm sets the increase and decrease ratio of the learning rate to achieve its adaptive adjustment. A(k) is the sum of squared errors of the kth iteration. The criterion for adjusting the learning rate is to check whether the current learning error is less than the previous learning error. If so, it indicates that the current iteration is effective and the current learning rate is suitable for the error change trend, and the learning rate can be appropriately increased. If the current learning error is greater than the previous learning error, it indicates overshoot, and the learning rate should be decreased. 2. System Simulation This method is applied to the diagnosis of intermittent and difficult faults in gasoline engines. Due to the mutual coupling between various engine components, engine faults have characteristics such as multi-level, fuzzy, and similarity. The correspondence between fault symptoms and faults often exhibits high nonlinearity and coupling, and fault information often exhibits high inaccuracy and uncertainty. Let y1-y5 represent the causes of the faults respectively: y1: intake manifold leakage; y2: upstream oxygen sensor failure; y3: engine computer failure; y4: airflow sensor failure; y5: fuel injector blockage. At idle, the engine operating parameters are represented by A, B, C, D, E, F, and G: A. Engine speed (r/min); B. Cylinder pressure (MPa); C. Throttle opening (%); D. Upstream oxygen sensor output voltage (mV); E. Fuel pressure (MPa); F. Injection pulse width (ms); G. Fuel trim value (%). Their normal operating values ​​are: 700, 0.91, 4.6, 0.5, 0.05, 2.2, 4.1 respectively. According to the method described in this paper, a three-layer BP neural network needs to be designed. Since seven signals need to be collected, after fuzzy preprocessing, the neural network has a total of 21 inputs and 5 outputs. Regarding the design of hidden layer units, Kolmogorov gave a theorem in 1947. This theorem states that a neural network with n input layers and 2n+1 hidden layers can accurately implement any continuous function. As the number of hidden layer units increases, the learning rate of the neural network decreases. However, the number of hidden layer units should not be increased unnecessarily, as an excessively large number of hidden layer units will increase computational complexity, thus increasing training time, and will also cause other problems. Therefore, it is generally advisable to add one or two hidden layer units only when the problem needs to be solved. Accordingly, this system selects 43 hidden layer units. The network was trained using MATLAB simulation tools. The allowable error range was set to 0.001, and the number of training iterations was set to 500. The training results are as follows: As can be seen from the figure above, the network converges to the allowable error range after 219 iterations. 3. Experiment Two sets of fault data were randomly selected to verify the system. Tables 2 and 3 are the system input and output data, respectively. The output results show that the two sets of data correspond to y1 and y3, which are consistent with the expert reasoning results. It can be seen that the system can effectively identify some faults of gasoline engines. 4. Conclusion The system introduction test confirms that the system has good fault identification ability. It can not only handle uncertain knowledge and ambiguous data well, but also improve the accuracy of fault diagnosis. Therefore, this method can be extended to fault diagnosis systems with large inertia, hysteresis, nonlinearity, and difficulty in determining the relationship between fault and symptoms, and has good application prospects. References: [1] Wu Jinpei. Fuzzy Diagnosis Theory and Its Application [M]. Beijing: Science Press, 1995. [2] Han Liqun. Artificial Neural Network Theory, Design and Application [M]. Beijing: Chemical Industry Press. 2002. [3] Zhong Shisheng, Pu Shuxue, Ding Gang. Application of Improved BP Algorithm in Process Neural Network. Journal of Harbin Institute of Technology. 2006, 6: 840～842. [4] Zhang Jianhua. Research on Fault Diagnosis Method Based on Fuzzy Neural Network [J]. Journal of Beijing University of Aeronautics and Astronautics. 1997, 8: 502～506. [5] Liu Weiqiang, Xu Xiangyang, Wang Shuhan. Engine Fault Diagnosis Method Based on Fuzzy Theory and Parallel Reasoning of Neural Network [J]. Journal of Shandong Jiaotong University. 2006, 9: 11～15. [6] Xiang Song, Wang Yu, Liu Guoquan. Predictive Model of Protective Slag Performance Based on Improved BP Algorithm Neural Network [J]. Steelmaking. 2006, 6: 45～48. [7] MY Chow, S Alrug, HJ Trussel1. Heuristic constraints enforcement for training of and knowledge extraction from a fuzzy-neural architecture part I-II. IEEE Transactions on Fuzzy Systems. 1999. 7 (2): 143-159. 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