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Designing a complex system generally requires its decomposition into smaller modular constituents for the ease of design, integration, operation, and future upgrades. Typically, system decomposition analysis is conducted using an abstract system model during the initial system design stage. The design structure matrix (DSM) has been a popular tool for abstract system modeling. In the DSM, systems are modeled and decomposed into modular configurations using published clustering algorithms. In this paper, we present a novel approach for system architecture decomposition and modularization based on the DSM, which can incorporate multiple design constraints into clustering algorithms. Two clustering algorithms are modified to accommodate the system design constraints. The modified algorithms are used to decompose a static inverter (SIV) of an electrical train into modular configurations that include various design constraints. Finally, the results and analysis of the SIV modularization using new modified clustering algorithms are presented.

A fault diagnosis of a train door system is carried out using the motor current signal that operates the door. A test rig is prepared, in which various fault modes are examined by applying extreme conditions, as well as the natural and artificial wears of critical components. Two approaches are undertaken toward the fault classification for comparative purposes: one is the traditional feature-based method that requires several steps for the processing features such as signal segmentation, the extraction of time-domain features, selection by Fisher’s discrimination, and K-nearest neighbor. The other is the deep learning approach by employing the convolutional neural network (CNN) to skip the hand-crafted features extraction process. In the traditional approach, good accuracy is found only after the current signal is segmented into the three velocity regimes, which enhances the discrimination capability. In the CNN, superior accuracy is obtained even by the original raw signal, which is more convenient in terms of implementation. However, in view of practical applications, the traditional approach is more useful in that the features processing can be easily applied to assess the health state of each fault and monitor the progression over time in the real operation, which is not enabled by the deep learning approach.

A study on reliability centered maintenance planning of a standard electric motor unit subsystem using computational techniques † AbstractThe design and manufacture of urban transportation applications has been necessarily complicated in order to improve its safety. Urban transportation systems have complex structures that consist of various electric, electronic, and mechanical components, and the maintenance costs generally take up approximately 60% of the total operational costs. Therefore, it is essential to establish a maintenance plan that takes into account both safety and cost. In considering safety and cost limitations, this research introduces an advanced reliability centered maintenance (RCM) planning method using computational techniques, and applies the method to a standard electric motor unit (EMU) subsystem. First, this research devises a maintenance cost function that can reflect the current operating conditions, and maintenance characteristics, of components by generating essential cost factors. Second, a reliability growth analysis (RGA) is performed, using the Army Material Systems Analysis Activity (AMSAA) model, to estimate reliability indexes such as failure rate, and mean time between failures (MTBF), of a standard EMU subsystem, and each individual component Third, two optimization processes are performed to ascertain the optimal maintenance reliability of each component in the standard EMU subsystem. Finally, this research presents the maintenance time of each component based on the optimal maintenance reliability provided by optimization processesand reliability indexes provided by the RGA method.

The reliability and safety of the train system is a critical issue, as it transports many passengers in its daily operation. Most studies focus on fault diagnosis methods to determine the cause of faults in the train system. Aside from fault diagnosis, it is also vital to perceive a fault even before it occurs. In this study, a fault occurrence prediction based on a machine learning model is developed. The fault occurrence prediction method aims to predict the remaining useful life (RUL) of a train subsystem. RUL refers to the remaining amount of time before a fault occurs on a train subsystem. The prediction method developed in this study can be used to clear a fault even before it occurs. In case of inevitable faults, the output from the prediction method can be used to alert the personnel in charge by imposing an alarm. Therefore, the fault occurrence prediction method is expected to increase the reliability of the train system. The deep neural-network-based model is tested on an actual device. Deep neural network is used because of its feature extraction capability, especially in handling big amount of data. The testing results in 90.08% accuracy. In addition, a graphical user interface is developed as an interface between a user and the actual device containing the fault occurrence prediction model.