Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Valve Fault Diagnosis: <s> Owing to the complex structure and the presence of a multiplicity of sources, the vibratory signals collected from diesel engines are always complicated in composition. The features extracted from one kind of faulty signal often overlap with those collected from other kinds of faulty signals. This phenomenon confuses the machine operators and usually misleads them into drawing a wrong conclusion. The use of effective features and the adoption of a powerful diagnostic strategy are two critical issues leading to successful diagnosis of machine faults. Both a specially designed Genetic Algorithm (GA) and the Kernel Principal Component Analysis (KPCA) technique were adopted in the paper, and an effective method has been achieved for diagnosing the faults occurring on the engine valves. Five kinds of valve faults are simulated on the sixth exhaust valve of a 6135-type diesel engine. The experiments fully prove that the proposed method is effective on valve fault diagnosis and has a great potential to be more widely accepted in industry. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Valve Fault Diagnosis: <s> Based on complementary strategies, a new AI method, the hybrid of ant colony algorithm and neural network, was put forward to solve the fault diagnosis of diesel engine. The ant colony algorithm is used to simplify attribute parameters reflecting operating conditions of diesel engine and in which unnecessary attributes are eliminated. According to the reduction result, the fault diagnosis system based on RBF neural network was produced. Through the comparison of fault classification effect, it is shown that the new method reduces the dimension of input to neural network, raises the training efficiency and the fault classification accuracy. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Valve Fault Diagnosis: <s> Abstract Aimed at the problem of the feature of nonstationary vibration signal can not be extracted easily; the bispectrum analysis technique was researched in this paper. Bispectral characteristic frequency faces were searched along the parallel to the diagonal line at certain step in the bispectral modulus field, and the mean magnitude was calculated to get the feature parameters. The method was applied in fault feature extraction of piston-pin of diesel engine successfully. Finally, the piston-pin faults were diagnosed associated with genetic neural network. <s> BIB003
In engine fault diagnosis systems, the features extracted from one type of faulty signals usually overlap with the ones collected from other faulty signals. This makes the problem of diagnosing the faults complicated. Thus, effectively finding the most discriminative features and the adoption of a powerful diagnosis strategy are two important ingredients of a good diagnosis algorithm. Considering these two aspects, in order to develop a diagnosis algorithm for valve fault diagnosis of a diesel engine, a genetic algorithm was proposed in BIB001 to find the best features, and then the Kernel principal component analysis technique was used for the diagnosis purpose. The algorithm was tested on the sixth exhaust valve of a 6135-type diesel engine, and the results showed effective performance. In a rather similar work, in order to diagnose the faults in a diesel engine, an ant colony algorithm was adopted in BIB002 to simplify the attribute parameters reflecting the operating conditions of the diesel engine, and then a RBF neural network was used to diagnose the faults. In  , genetic programming was employed to diagnose the faults of the valves of a six cylinder/four stroke cycle diesel engine in three different valve states: the normal condition, valve-tapped clearance, and gas leakage faults. The authors applied a power-weight coefficient to each feature to construct the diagnostic tree more intelligently. They designed 22 mathematical functions and eight signal features to construct the diagnostic model. Additionally, they employed different evolution strategies for selecting the functions and features. 2) Piston Pin Fault Diagnosis: In order to diagnose the fault feature extraction of piston-pin of a diesel engine, a genetic neural network was employed in BIB003 . As the features of nonstationary vibration signals cannot easily be extracted, the bispectrum analysis technique was studied in this research, where the bispectral characteristics frequency faces are searched along the parallel to the diagonal line at certain steps in the bispectral modulus field. Then the mean magnitude was calculated to obtain the feature parameters. 3) Fault Diagnosis of Fuel Systems: The fault diagnosis of the fuel system in a diesel engine was addressed in  , where using a bootstrap, the data acquired from a running engine were preprocessed. Then a genetic programming algorithm was developed which finds an efficient tree-like structure of a group of initial candidate features. The proposed algorithm in this paper found the best compound feature and showed promising results.