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CONTENTS
Volume 22, Number 2, August 2018
 

Abstract
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Key Words
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Address
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Abstract
Increasing interest in prognostics and health management has heightened the need for wireless sensor networks (WSN) with efficient power sources. Piezoelectric energy harvesters using Pb(Zr,Ti)O3 (PZT) are one of the candidate power sources for WSNs as they efficiently convert mechanical vibration energy into electrical energy. These types of devices are resonated at a specific frequency, which has a significant impact on the amount of energy harvested, by external vibration. Hence, precise prediction of mechanical deformation including modal analysis of piezoelectric devices is crucial for estimating the energy generated under specific conditions. In this study, an experimental vibrational system capable of controlling a wide range of frequencies and accelerations was designed to generate mechanical vibration for piezoelectric energy harvesters. In conjunction with MATLAB, the system automatically finds the resonance frequency of harvesters. A small accelerometer and non-contact laser displacement sensor are employed to investigate the mechanical deformation of harvesters. Mechanical deformation under various frequencies and accelerations were investigated and analyzed based on data from two types of sensors. The results verify that the proposed system can be employed to carry out vibration experiments for piezoelectric harvesters and measurement of their mechanical deformation.

Key Words
piezoelectric; beam shape; energy harvester; PZT; mechanical deformation

Address
Changho Kim, Youngsu Ko, Taemin Kim, Youngho Kim1 and Namsu Kim: Department of Mechanical Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul, Republic of Korea
Chan-Sei Yoo and Seung Ho Han: Electronic Convergence Materials & Device Research Center, Korea Electronics Technology Institute, 25 Saenari-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea
BeomJin Choi and YongHo Jang: Technical Research Center, SENBOL Inc., 437-5 Nonhyeon-dong, Namdong-gu, Incheon, Republic of Korea



Abstract
A variable electromotive-force generator (VEG), which is a modified generator with an adjustable overlap between the rotor and the stator, is proposed to expand the operational range of a regular generator through a simple and robust active control strategy. It has a broad range of applications in hybrid vehicles, wind turbines, water turbines, and similar technologies. A mathematical model of the VEG is developed, and a novel prototype is designed and fabricated. The performance of the VEG with an active control system, which adjusts the overlap ratio based on the desired output power at different rotor speeds for a specific application, is theoretically and experimentally studied. The results show that reducing the overlap between the rotor and the stator of the generator results in reduced torque loss of the generator and an increased rotational speed of the generator rotor. A VEG can improve the fuel efficiency of hybrid vehicles; it can also expand operational ranges of wind turbines and water turbines and harness more power.

Key Words
modeling; design; variable electromotive-force generator with an adjustable overlap between the rotor and the stator; hybrid vehicle; wind turbine; active control system

Address
W.D. Zhu, X.F. Wang and P. Kendrick: Department of Mechanical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
N. Goudarzi: Mechanical Engineering Technology Program, Department of Engineering Technology and Construction Management, University of North Carolina at Charlotte, Charlotte, NC 28223, USA


Abstract
Maintenance activities are regarded as a key part of the repairable deteriorating system because they maintain the equipment in good condition. In practice, many maintenance policies are used in engineering fields to reduce unexpected failures and slow down the deterioration of the system. However, in traditional maintenance policies, maintenance activities have often been assumed to be performed at the same time interval, which may result in higher operational costs and more system failures. Thus, this study presents two non-periodic preventive maintenance (PM) policies for repairable deteriorating systems, employing the failure rate of the system as a conditional variable. In the proposed PM models, the failure rate of the system was restored via the failure rate reduction factors after imperfect PM activities. Operational costs were also considered, which increased along with the operating time of the system and the frequency of PM activities to reflect the deterioration process of the system. A numerical example was provided to illustrate the proposed PM policy. The results showed that PM activities performed at a low failure rate threshold slowed down the degradation of the system and thus extended the system lifetime. Moreover, when the operational cost was considered in the proposed maintenance scheme, the system replacement was more cost-effective than frequent PM activities in the severely degraded system.

Key Words
preventive maintenance; failure rate threshold; failure rate reduction factor; operational cost; minimal repair

Address
Juhyun Lee and Jihyun Park: Department of Industrial and Management Engineering, Hanyang University, Seoul,15588, Republic of Korea
Suneung Ahn: Department of Industrial and Management Engineering, Hanyang University ERICA Campus, Ansan, 04763, Republic of Korea

Abstract
In the last decade, there has been an exponential increase of scientific interest in smart additive manufacturing (AM) technology. Among the different AM techniques, one of the most commonly applied processes is digital light processing (DLP). DLP uses a digital projector screen to flash an ultraviolet light which cures photopolymer resins. The resin is cured to form a solid to produce parts with precise high dimensional accuracy. During the curing process, there are several process parameters that need to be optimized. Among these, the exposure time affects the quality of the 3D printed specimen such as mechanical strength and dimensional accuracy. This study examines optimal exposure times and their impact on printed part. It was found that there is optimal exposure time for printed part to have appropriate mechanical strength and accurate dimensions. The gel fraction and TGA test results confirmed that the improvement of mechanical properties with the increasing UV exposure time was due to the increase of crosslinked network formation with UV exposure time in acrylic resins. In addition, gel fraction and thermogravimetric analysis were employed to microscopically investigate how this process parameter impacts mechanical performance.

Key Words
additive manufacturing; DLP 3-D Printing; mechanical properties; dimensional accuracy, UV curing, acrylic resin

Address
Younghun Lee, Xing Guan Zhao, Dongoh Lee, Taemin Kim, Hoeryong Jung and Namsu Kim: Department of Mechanical Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Sungho Lee: SoC Platform Research Center, Korea Electronics Technology Institute, 25 Saenari-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 13509, Republic of Korea


Abstract
Digital input and output modules are widely used to connect digital sensors and actuators to automation systems. Digital I/O modules provide flexible connectivity extension to numerous sensors and actuators and protect systems from high voltages and currents by isolation. Components in digital I/O modules are inevitably affected by operating and environmental conditions, such as high voltage, high current, high temperature, and temperature cycling. Because digital I/O modules transfer signals or isolate the systems from unexpected voltage and current transients, their failures may result in signal transmission failures and damages to sensitive circuitry leading to system malfunction and system shutdown. In this study, the lifetime of optocouplers, one of the critical components in digital I/O modules, was predicted using Bayesian tracking approaches. Accelerated degradation tests were conducted for collecting the critical performance parameter of optocouplers, current transfer ratio (CTR), during their lifetime. Bayesian tracking approaches, including extended Kalman filter and particle filter, were applied to predict the failure. The performance of each prognostic algorithm was then compared using accuracy and robustness-based performance metrics.

Key Words
digital input and output modules; optocouplers; lifetime prediction; particle filter; extended Kalman filter Bayesian tracking approaches

Address
Insun Shin: Department of System Design and Control Engineering, Ulsan National Institute of Science and Technology,Ulsan, 44919, Republic of Korea
Daeil Kwon: School of Mechanical, Aerospace and Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea



Abstract
Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the \"residual fitting\" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

Key Words
convolutional neural networks; machine learning; deep learning; time-series analysis

Address
Seungtae Park, Hojin Lee and Seungchul Lee: Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
Haedong Jeong: Department of System Design and Control, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
Hyungcheol Min: Korea Electric Power Corporation Research Institute, Daejeon, Republic of Korea

Abstract
Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain (QAHSS), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

Key Words
gearbox; failure prediction; noise; fault diagnosis; prognostics and health management (PHM)

Address
Jungho Park, Byungjoo Jeon, Jongmin Park and Myungyon Kim: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
Jinshi Cui: OnePredict. Inc, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
Byeng D. Youn: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea;
OnePredict. Inc, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea


Abstract
From the viewpoint of engineering applications, the prediction of the failure of bogies plays an important role in preventing the occurrence of fatigue. Fatigue is a complex phenomenon affected by many uncertainties (such as load, environment, geometrical and material properties, and so on). The key to predict fatigue damage accurately is how to quantify these uncertainties. A Bayesian model is used to account for the uncertainty of various sources when predicting fatigue damage of structural components. In spite of improvements in the design of fatigue-sensitive structures, periodic non-destructive inspections are required for components. With the help of modern nondestructive inspection techniques, the fatigue flaws can be detected for bogie structures, and fatigue reliability can be updated by using Bayesian theorem with inspection data. A practical fatigue analysis of welded bogies is utilized to testify the effectiveness of the proposed methods.

Key Words
bogies fatigue; Bayesian; uncertain; crack growth; nondestructive inspection

Address
Fang-Jun Zuo: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China;
College of Mechanical engineering, Chengdu Technological University No. 1, Zhongxin Avenue, Pidu Distric, Chengdu, Sichuan, 611730, P.R. China
Yan-Feng Li and Hong-Zhong Huang: Center for System Reliability and Safety, University of Electronic Science and Technology of China No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China



Abstract
This paper proposes a line laser thermography scanning (LLTS) system for multiple crack evaluation on a concrete structure, as the core technology for unmanned aerial vehicle-mounted crack inspection. The LLTS system consists of a line shape continuous-wave laser source, an infrared (IR) camera, a control computer and a scanning jig. The line laser generates thermal waves on a target concrete structure, and the IR camera simultaneously measures the corresponding thermal responses. By spatially scanning the LLTS system along a target concrete structure, multiple cracks even in a large scale concrete structure can be effectively visualized and evaluated. Since raw IR data obtained by scanning the LLTS system, however, includes timely- and spatially-varying IR images due to the limited field of view (FOV) of the LLTS system, a novel time-spatial-integrated (TSI) coordinate transform algorithm is developed for precise crack evaluation in a static condition. The proposed system has the following technical advantages: (1) the thermal wave propagation is effectively induced on a concrete structure with low thermal conductivity of approximately 0.8 W/m K; (2) the limited FOV issues can be solved by the TSI coordinate transform; and (3) multiple cracks are able to be visualized and evaluated by normalizing the responses based on phase mapping and spatial derivative processes. The proposed LLTS system is experimentally validated using a concrete specimen with various cracks. The experimental results reveal that the LLTS system successfully visualizes and evaluates multiple cracks without false alarms.

Key Words
concrete crack evaluation; IR thermography; line laser scanning; time-spatial-integrated coordinate transform; phase mapping process; nondestructive test

Address
Keunyoung Jang and Yun-Kyu An: Department of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea


Abstract
Efflorescence is a phenomenon primarily caused by a carbonation process in concrete structures. Efflorescence can cause concrete degradation in the long term; therefore, it must be accurately assessed by proper inspection. Currently, the assessment is performed on the basis of visual inspection or image-based inspection, which may result in the subjective assessment by the inspectors. In this paper, a novel approach is proposed for the objective and quantitative assessment of concrete efflorescence using hyperspectral imaging (HSI). HSI acquires the full electromagnetic spectrum of light reflected from a material, which enables the identification of materials in the image on the basis of spectrum. Spectral angle mapper (SAM) that calculates the similarity of a test spectrum in the hyperspectral image to a reference spectrum is used to assess efflorescence, and the reference spectral profiles of efflorescence are obtained from theUSGS spectral library. Field tests were carried out in a real building and a bridge. For each experiment, efflorescence assessed by the proposed approach was compared with that assessed by image-based approach mimicking conventional visual inspection. Performance measures such as accuracy, precision, and recall were calculated to check the performance of the proposed approach. Performance-related issues are discussed for further enhancement of the proposed approach.

Key Words
hyperspectral imaging; efflorescence; concrete; spectral angle mapper

Address
Byunghyun Kim and Soojin Cho: Department of Civil Engineering, University of Seoul,163 Seoulsiripdae-ro, Dondaemun-gu, Seoul 02504, Republic of Korea


Abstract
This study examines a non-contact laser scanning-based ultrasound system, called an angular scan pulse-echo ultrasonic propagation imager (A-PE-UPI), that uses coincided laser beams for ultrasonic sensing and generation. A laser Doppler vibrometer is used for sensing, while a diode pumped solid state (DPSS) Q-switched laser is used for generation of thermoelastic waves. A high-speed raster scanning of up to 10-kHz is achieved using a galvano-motorized mirror scanner that allows for coincided sensing and for the generation beam to perform two-dimensional scanning without causing any harm to the surface under inspection. This process allows for the visualization of longitudinal wave propagation through-the-thickness. A pulse-echo ultrasonic wave propagation imaging algorithm (PE-UWPI) is used for on-the-fly damage visualization of the structure. The presented system is very effective for high-speed, localized, non-contact, and non-destructive inspection of aerospace structures. The system is tested on an aluminum honeycomb sandwich with disbonds and a carbon fiber-reinforced plastic (CFRP) honeycomb sandwich with a layer overlap. Inspection is performed at a 10-kHz scanning speed that takes 16 seconds to scan a 100 X 100 mm2 area with a scan interval of 0.25 mm. Finally, a comparison is presented between angular-scanning and a linear-scanning-based pulse-echo UPI system. The results show that the proposed system can successfully visualize defects in the inspected specimens.

Key Words
pulse echo laser ultrasound; laser Doppler vibrometer; laser scanning; non-destructive evaluation

Address
Syed H. Abbas and Jung-Ryul Lee: Department of Aerospace Engineering, Korean Advanced Institute for Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea


Abstract
A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

Key Words
compressive sensing; condition monitoring; receiver operating characteristic; variance considered machine

Address
Myung Jun Lee, Jun Young Jun and Gyuhae Park: School of Mechanical Engineering, Chonnam National University, Gwangju, South Korea
To Kang and Soon Woo Han: Nuclear Convergence Technology Division, Korea Atomic Energy Research Institute, Daejeon, South Korea

Abstract
A magnetic flux leakage (MFL) method was applied to detect and quantify defects in a steel bar. A multi-channel MFL sensor head was fabricated using Hall sensors and magnetization yokes with permanent magnets. The MFL sensor head scanned a damaged specimen with five levels of defects to measure the magnetic flux density. A series of signal processing procedures, including an enveloping process based on the Hilbert transform, was performed to clarify the flux leakage signal. The objective damage detection of the enveloped signals was then analyzed by comparing them to a threshold value. To quantitatively analyze the MFL signal according to the damage level, five kinds of damage indices based on the relationship between the enveloped MFL signal and the threshold value were applied. Using the proposed damage indices and the general damage index for the MFL method, the detected MFL signals were quantified and analyzed relative to the magnitude of the damage increase.

Key Words
magnetic flux leakage; steel bar inspection; damage quantification; Hilbert transform; generalized extreme value distribution

Address
Ju-Won Kim and Seunghee Park: School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, 2066 Seobu-ro,
Jangan-gu, Suwon 16419, Republic of Korea
Minsu Park and Junkyeong Kim: Department of Civil & Environmental System Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea




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