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Convolutional Neural Network (CNN) is, in general, good at finding principal components of data. However, the characteristic components of the signals could often be obscured by system noise. Therefore, even though the CNN model is well-trained and predict with high accuracy, it may detect only the primary patterns of data which could be formed by system noise. They are, in fact, highly vulnerable to maintenance activities such as reassembly. In other words, CNN models could misdiagnose even with excellent performances. In this study, a novel method that combines the classification using CNN with the data preprocessing is proposed for bearing fault diagnosis. The proposed method is demonstrated by the following steps. First, training data is preprocessed so that the noise and the fault signature of the bearings are separated. Then, CNN models are developed and trained to learn significant features containing information of defects. Lastly, the CNN models are examined and validated whether they learn and extract the meaningful features or not.

Air-operated valves (AOVs) are used to control or shut off the flow in the nuclear power plants. In particular, the failure of safety-related AOV could have significant impacts on the safety of the nuclear power plants and therefore, their performances have been tested and evaluated periodically. However, the current method to evaluate the performance needs to be revised to enhance the accuracy and to identify defects of AOV independently of personal skills. This paper introduce the ANN (Artificial Neural Network) model to diagnose the performance and the condition altogether. Test facilities were designed and configured to measure the signals such as supply pressure, control pressure, actuator pressure, stem displacement and stem thrust. Tests were carried out in various conditions which simulate defects with leak/clogged pipes, the bent stem and so on. First, the physical models of an AOV are developed to describe its behavior and to parameterize the characteristics of each component for evaluating the performance. Secondly, CNN (Convolutional Neural Network) architectures are designed considering the developed physical models to make a lead to the optimal performance of ANN. To train the ANN effectively, the measured signals were divided into several regions, from each of which the features are extracted and the extracted features are combined for classifying the defects. In addition, the model can provide the parameters of maximum available thrust, which is the key factor in periodic verification of AOV with the required accuracy and classify more than 10 different kinds of defects with high accuracy.

-In this paper, we propose a electrical/mechanical method to effectively diagnose the local deterioration of a 10m long power shielded twist pair cable defined by the American Wire Gauge (AWG) 14 specification using electrical/mechanical methods. The rapid deterioration of the cable proceeded by using the heating furnace, which is based on the Arrhenius equations proceeds from 0 to 35 years with the deteriorated equivalent model. In this paper, we introduce a method to diagnose the characteristics of locally deteriorated cable by using S 21 phase and frequency change rate measured by vector network analyzer which is the electrical diagnostic method. The measured S 21 phase and rate of change of frequency show a constant correlation with the number of years of locally deteriorated cable, thus it can be useful for diagnosing deteriorated cables. The change of modulus due to deterioration was measured by a modulus measuring device, which is defined by the ratio of deformation from the force externally applied to the cable, and the rate of modulus change also shows a constant correlation with the number of years of locally deteriorated cable. Finally, By combining the advantages of electrical/ mechanical diagnostic methods, we can efficiently diagnose the local deterioration in the power shielded cable.

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