Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> E. Fault Diagnosis in Diesel Engines <s> On the point view of complementary strategies, a new hybrid algorithm to optimize the RBF network based on artificial immunology was proposed. A dynamic clustering algorithm based on clonal selection algorithm was used to specify the amount and initial position of the RBF centers; then RBF network was trained by the immune evolutionary algorithm. Combining with the rough sets-based attribute reduction algorithm, a novel hybrid system of rough sets and Immune-RBF network for intelligent fault diagnosis were put forward. The diagnosis of diesel demonstrates that the method can effectively simplify the structure of network, and increase the efficiency and precision of diagnosis. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> E. Fault Diagnosis in Diesel Engines <s> In the present study, an expert system is developed to identify and classify fault condition for diesel engine. Vibration signals are collected on a diesel engine test platform. Wavelet packet analysis (WPA) coefficients of vibration signals are used for evaluating their Shannon entropy and treated as the features to identify the fault conditions of diesel engine in the preprocessing. A back-propagation neural network (BPNN) is used to classify the fault condition. To improve the convergence of BPNN, a hybrid particle swarm optimization (PSO) with a differential operator named PSO-DV is used to adjust the weights and threshold of BPNN in fault diagnosis of diesel engine. To verify the proposed PSO-DV hybrid method has the better convergence, a classical PSO based BPNN is compared with a PSO-DV based BPNN in fault classification of diesel engine. The experimental results showed the proposed hybrid intelligent PSO-DV method not only achieved classification for diesel engine, but also can escape from local optima, so has better convergence than classical PSO. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> E. Fault Diagnosis in Diesel Engines <s> The characteristic vector of cylinder vibration signal is extracted by wavelet packet decomposition. The factor of selection is proposed to take adaptive integration on basis of improved super parent one dependence estimator Bayesian method and back propagation genetic algorithm neural network method. Experimental results on WD615 diesel engine showed that the method has high accuracy rate of detection. <s> BIB003
In order to diagnose the faults in a diesel engine, a radial basis function (RBF) network was used in BIB001 . Then based on a clonal selection algorithm, a dynamic clustering algorithm was adopted to specify the initial positions of the RBF centers and an immune EA was employed to train the network. In BIB002 , the vibration signals from a diesel engine were collected and then wavelet packets analysis coefficients of vibration signals were used to evaluate their Shannon entropy. These coefficients were then used as features to diagnose the faults of the engine. The data were then fed to a backpropagation neural network, and a hybrid PSO algorithm with a differential operator were adopted to adjust the weights of the network. In a similar approach BIB003 , the cylinder vibration signal of a diesel engine was extracted and transformed into the wavelet coefficients. Then a back propagation genetic algorithm neural network method was employed to diagnose the faults in the engine.