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Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.

A variety of sensor systems have been developed to monitor the structural health status of buildings and infrastructures. However, most sensor systems for structural health monitoring (SHM) are difficult to use in extreme conditions, such as a fire situation, because of their vulnerability to high temperature and physical shocks, as well as time-consuming installation process. Here, we present a smart ball sensor (SBS) that can be immediately installed on surfaces of structures, stably measure vital SHM data in real time and wirelessly transmit the data in a high-temperature fire situation. The smart ball sensor mainly consists of sensor and data transmission module, heat insulator and adhesive module. With the integrated device configuration, the SBS can be strongly attached to the target surface with maximum adhesion force of 233.7-N and stably detect acceleration and temperature of the structure without damaging the key modules of the systems even at high temperatures of up to 500 &deg, C while ensuring wireless transmission of the data. Field tests for a model pre-engineered building (PEB) structure demonstrate the validity of the smart ball sensor as an instantly deployable, high-temperature SHM system. This SBS can be used for SHM of a wider variety of structures and buildings beyond PEB structures.

While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. However, the problem is that different results can be derived from each regime and they can conflict with each other, which may invalidate the performance of fault diagnosis. To address this challenge, a methodology for selecting the most reliable one among the different diagnostic results is proposed, which combines the Bayesian network (BN) and the information value (IV). The BN is trained for each regime and a conditional probability table is obtained for probabilistic fault diagnosis. The IV is then employed to evaluate the value of several diagnostic results. The proposed approach is applied to the fault diagnosis of a train door system and its effectiveness is proven.

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&rsquo, 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.

In this paper, we are interested in computing ZIP code proximity from two perspectives, proximity between two ZIP codes (Ad-Hoc) and neighborhood proximity (Top-K). Such a computation can be used for ZIP code-based target marketing as one of the smart city applications. A naive approach to this computation is the usage of the distance between ZIP codes. We redefine a distance metric combining the centroid distance with the intersecting road network between ZIP codes by using a weighted sum method. Furthermore, we prove that the results of our combined approach conform to the characteristics of distance measurement. We have proposed a general and heuristic approach for computing Ad-Hoc proximity, while for computing Top-K proximity, we have proposed a general approach only. Our experimental results indicate that our approaches are verifiable and effective in reducing the execution time and search space.

In the vehicular networks, the real-time video reporting service is used to send the recorded videos in the vehicle to the cloud. However, when facilitating the real-time video reporting service in the vehicular networks, the usage of the fourth generation (4G) long term evolution (LTE) was proved to suffer from latency while the IEEE 802.11p standard does not offer sufficient scalability for a such congested environment. To overcome those drawbacks, the fifth-generation (5G)-enabled vehicular network is considered as a promising technology for empowering the real-time video reporting service. In this paper, we note that security and privacy related issues should also be carefully addressed to boost the early adoption of 5G-enabled vehicular networks. There exist a few research works for secure video reporting service in 5G-enabled vehicular networks. However, their usage is limited because of public key certificates and expensive pairing operations. Thus, we propose a secure and lightweight protocol for cloud-assisted video reporting service in 5G-enabled vehicular networks. Compared to the conventional public key certificates, the proposed protocol achieves entities’ authorization through anonymous credential. Also, by using lightweight security primitives instead of expensive bilinear pairing operations, the proposed protocol minimizes the computational overhead. From the evaluation results, we show that the proposed protocol takes the smaller computation and communication time for the cryptographic primitives than that of the well-known Eiza-Ni-Shi protocol.

An Acaligense sp.-immobilized biosensor was fabricated based on QD-MWNT composites as an electron transfer mediator and a microbe immobilization support by a one-step radiation reaction and used for sensing phenolic compounds in commercial red wines. First, a quantum dot-modified multi-wall carbon nanotube (QD-MWNT) composite was prepared in the presence of MWNT by a one-step radiation reaction in an aqueous solution at room temperature. The successful preparation of the QD-MWNT composite was confirmed by XPS, TEM, and elemental analysis. Second, the microbial biosensor was fabricated by immobilization of Acaligense sp. on the surface of the composite thin film of a glassy carbon (GC) electrode, which was prepared by a hand casting method with a mixture of the previously obtained composite and Nafion solution. The sensing ranges of the microbial biosensor based on CdS-MWNT and Cu(2)S-MWNT supports were 0.5-5.0 mM and 0.7-10 mM for phenol in a phosphate buffer solution, respectively. Total concentration of phenolic compounds contained in commercial red wines was also determined using the prepared microbial immobilized biosensor.

A modified electrode for hydrogen peroxide (H2O2) sensing was prepared via thiophene (Th) with epoxy group. Thiophene (EpoxyTh) with epoxy group was synthesized by reaction of 3-bromothiophene and glycidyl methacrylate (GMA) in acetonitrile according to Heck Reaction. The electrocopolymerization of Th and EpoxyTh was performed on the surface of indium tin oxide (ITO) electrode by cycling the potential between -1.0 and +2.5 V in mixture of thiophene (Th) and EpoxyTh. Poly(Th-co- EpoxyTh) grown onto the ITO electrode was successfully confirmed by SEM, AFM, and water contact angle analysis, respectively. Finally, the HRP was immobilized on the surface of poly(Th-co-EpoxyTh) electrode by covalent binding. The amperometric response of the HRP-immobilized poly(Th-co-EpoxyTh) electrode for H2O2 was examined by cyclic voltammetry (CV). The HRP-immobilized poly(Th-co-EpoxyTh) electrode showed linearity from 0.1 to 30 mM H2O2, good reproducibility, and long life time.

Enzyme modified electrodes were fabricated with poly (aniline-co-m-aminophenol). Electrochemical polymerization of aniline and m-aminophenol was performed to get the film of copolymer on the surface of gold electrode. Modified electrodes were fabricated by two methods, physical entrapment and covalent cross-linking. In one of the method, gold nanoparticles were loaded into the copolymer film and horseradish peroxidase (HRP) was immobilized into the Au nanoparticle loaded copolymer film through physical entrapment. In the other method, the amino and -OH groups in the copolymer are utilized to form covalent functionalization with HRP via glutaric dialdehyde as cross-linker/mediator. The conducting copolymer/enzyme modified electrodes prepared by physical entrapment/covalent functionalization of enzyme were tested for electrocatalytic activities towards sensing of H2O2. Amperometric results indicate that enzyme modified electrode via physical entrapment possesses better electrocatalytic performance over covalent functionalized enzyme electrode.