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The brake system is very important part of the machine working. If the machine can't be stopped, it would be serious accident. It is the same as the ship. The mooring winch brake hold the ship in harbor. But sometimes it appeared the excessive stress and brake lining would be broken. So it is necessary to change the shape of brake system with improve the braking power. This study carried out research on optimum design like as moved the position of link pin or modified the thickness of brake band and lining.
An compressor that is combined with the rotor and pneumatic technology has been researching for the performance of pressure. However, the control of operations, an accurate diagnosis and the maintenance of compressor system are limited though the simple structure of compressor and compression are advantaged to reduce the energy. In this paper, the characteristic of the compressor operating under the normal or abnormal condition is realized. and the efficient diagnosis method is proposed through feature based analysis. Also, by using the GA (genetic algorithm) and SVM (support vector machine) of machine learning, the performance of feature analysis is conducted. Different misalignment mode of learning data for compressor is evaluated using the fault simulator. Therefore, feature based analysis is conducted considering misalignment mode of the compressor and the possibility of a diagnosis of misalignment is evaluated.
Water injection pump produced the 1st oil well through the high pressure after the Deep water oil well drilling. After finish the work it is hard to produce only using itself pressure due to low pressure. Therefore it can be increased recovery factor through the injection seawater of high pressure. Is the key equipment used in the marine plant and it is developing at many industries. In this paper, Analyze changes in the natural frequency due to the stiffness of the bearing. Analyze the critical speed of the natural frequency due to the change of operation speed. And evaluate the Stability. And then analyze the displacement and clearance through the unbalance response this way has contributed to the reliability of the developing product. Through a mathematical analysis.
Ultrasound signal is widely used to detect fault by heterodyned signal. Typically an expert will scan around the object with the scanning module while listening through headphones and observing a display panel. But this diagnosis procedure is required by specialized expert and hardly detect early defect. In this paper, Feature selection based on GA (genetic algorithms) is selected from the features of ultrasound signal on frequency domain and time domain. Then, by using the Support Vector Machine one of the machine learning, the performance of classification is evaluated by extracted features and selected features. The results of classification is compared with feature extraction based on PCA (principal component analysis). Therefore, the feature selected for each defect can be used as a reference by feature analysis for ultrasound.