The failure diagnosis of railway vehicle door system is carried out using a test bench and machine learning software for the fast and accurate classification. The signal length deviation exists in actual collected data of normal operation and abnormal failures with a time delay. The traditional data multi-segmentation technique for feature extraction has shortcomings by assuming that the measured time-based signals have the same operating time. However, the uniform data segmentation has a difficulty due to the deviation of measured data length. A method of converting time-based data into position-based data was performed to overcome the deviation problem. A method of optimized single-zone data using a genetic algorithm was proposed to improve the classification performance and to reduce computation time, instead of existing the multi-segmentation technique. A principal component analysis-based feature dimensional reduction with explained variance ratio was used to reduce the effect from multi-collinearity of features. Finally, the combination of the proposed methods was compared with individual methods to validate the classification performance by using support vector machine and other classifiers. It was confirmed that the proposed combination method shows the highest classification accuracy of 99.84%. INDEX TERMS failure diagnosis, genetic algorithm, principal component analysis, railway vehicle doors
The Brake operating unit (BOU) of a railway vehicle is one of the important systems for controlling the braking of the train. Because this system uses compressed air, it is difficult to understand and train the system. The existing education method involves learning the pneumatic flow of various control air in a 2D pneumatic circuit diagram based on a maintenance manual. However, in the actual braking system, it was difficult to learn effectively because the air flows in 3D. In order to solve these problems, the improvement of the training technique using the new 3D augmented reality (AR) was performed. In this study, to increase the learning effect of air brake flow, a technique for simultaneously displaying the pneumatic flow in 2D circuit diagram and 3D model was proposed. First, the distance ratio for simultaneous display can be determined using the proposed streamline matching variable calculation algorithm (SLMVC) that uses position and animation duration as input variables. Second, to avoid the complexity of using the 24 variables of the Particle System module in Unity, an existing universal 3D platform, a continuously emission property correction algorithm (CEPC) that can output particle objects as a streamline using only 4 properties (e.g., start lifetime, start speed, emission rate over time, start delay). As a result, the following 6 different types of BOU air pressure could be simultaneously displayed in 2D and 3D (e.g., AC, BC, SR, SBR, AS1, AS2). Therefore, maintenance staff can effectively learn complex pneumatic flow. To verify the usability of the developed content, a survey using the NASA-TLX technique was conducted targeting 60 maintenance staff. As a result of the comparison between Group A using the existing maintenance manual and Group B using the developed AR content, the perceived workload decreased by 28%. In particular, the frustration part decreased by 64% and the performance part decreased by 62%, indicating that the usability of AR content was very good.INDEX TERMS Railway vehicle, augmented reality, brake operating unit, pnematic flow, particle effect.The associate editor coordinating the review of this manuscript and approving it for publication was Jesus Felez .
<abstract> <p>Transportation is among the more vital economic activities for a business and our daily life actions. At present, transport is one of the key branches playing a crucial role in the development of the economy. Transportation decision-making looks for ways to solve current and anticipated transportation problems while avoiding future problems. An interval-valued complex fuzzy set (IVCFS) is an extended form of fuzzy, interval-valued fuzzy and complex fuzzy sets, and it is used to evaluate complex and inaccurate information in real-world applications. In this research, we aim to examine the novel concept of IVCF soft relations (IVCFSRs) by utilizing the Cartesian product (CP) of two IVCF soft sets (IVCFSSs), which are determined with the help of two different concepts, referred to as IVCF relation and soft sets. Moreover, we investigated various types of relations and also explained them with the help of some appropriate examples. The IVCFSRs have a comprehensive structure discussing due dealing with the degree of interval-valued membership with multidimensional variables. Moreover, IVCFSR-based modeling techniques are included, and they use the score function to select the suitable transportation strategy to improve the value of the analyzed data. Finally, to demonstrate the effectiveness of the suggested work, comparative analysis with existing methods is performed.</p> </abstract>
To assure the safety of the power bogies for train, it is important to perform the durability analysis of reduction gear considering a variation of velocity and traction motor capability. In this study, two types of applied load histories were constructed from driving histories considering the tractive effort and the train running curves by using dynamic analysis software (MSC.ADAMS). Moreover, this study was performed by evaluating fatigue damage of the reduction gears for rolling stock using durability analysis software (MSC.FATIGUE). The finite element model for evaluating the carburizing effect on the gear surface was used for predicting the fatigue life of the gears. The results showed that the fatigue life of the reduction gear would decrease with an increasing numbers of stops at station.
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