bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Neural Network Based Fault Detection for Three-Phase Induction Motor
Authors: Cha Sunghwan , Lee Simon , Jo Jun , Lee Yong Oh , Hwang Jongwoon
Published: March 2024

The three-phase induction motor is known as one of the most widely-used machine in the manufacturing industry. Electrical and mechanical fault of this machine is able to cause breaking down the plant facilities that leads significant productivity losses. In this paper, we will propose the fault detection method of induction motor by using deep neural network with measurement data of the electrical current signals to provide supervised classification of 2 types ofmotor fault, rotor broken and stator conductor fault. We also studied the size of data-set in faulty state to train the model for fault detection.
Monitoring micro-damage in Lithium-ion battery by analysing acoustic emission signal
Authors: Lee Seung-Mi , Byeon Jai-Won
Published: March 2024

Lithium metal battery was evaluated by using acoustic emission (AE) technique. AE measurement system was set for acquiring an AE signal during accelerated charge/discharge cyclic test. Particle cracking, the formation of solid electrolyte interphase (SEI) layer, and cracking of the SEI layer were confirmed by microstructural observation after the cycle test. The charge/discharge process was divided into four stages (i.e., A, B, C, and D) based on the relation between state-of-voltage and damages. AE Signals in each stage were successfully detected and correlated with the damage mechanism. The AE time-domain characteristics (i. e., waveform, amplitude, and duration) in each stage were similar. On the other hand, AE signal could be classified into two distinct groups (i.e., high and low frequency) on basis of the frequency characteristic. Stages containing high-frequency components of 250-300 kHz were related with cracking phenomenon, while low-frequency AE signals in different stage were emitted from SEI layer formation. Based on correlation between frequency-domain characteristic and damage type on the electrode, In-situ evaluation of the damage progression in lithium metal battery by monitoring AE activities for the classified signal is suggested.
Instantaneous Detection of the Occurrence of Mechanical Resonances
Authors: Song Won Joon
Published: March 2024

In this paper, the kurtosis of time-domain signal is suggested as a detector of a mechanical resonance (MR). The statistical quantity is dependent on the shape of the statistical distribution of a given signal. Mechanical structures under environment noise vibrate in random pattern. If an eigenmode of the structure is stimulated, the sinusoidal pattern starts to develop in the vibration signal. Once MR occurs, near-resonance fluctuation becomes prominent in the time trace of the signal and alters the noisy signal of unimodal distribution into a near-sinusoidal oscillation of bimodal distribution. The value of kurtosis of the time-domain signal drops from around 3.0, then approaches close to 1.5 as MR progresses. The feasibility of kurtosis as an indicator of MR was tested with the piezoelectric responses obtained from microfabricated beams under acoustic stimulations. The test showed that the statistical parameter instantaneously detected the occurrence of MR elapsing quickly in this case. Investigation with the experimental data justified the use of the parameter as an instantaneous indicator of MR.
Introduction of NIMS Signal Analysis Method in NPP
Authors: Lee Sang-Guk
Published: March 2024

The NIMS, NSSS(nuclear steam supply system) Integrity Monitoring System, is designed to provide an integrated approach that includes areas of monitoring relevant to the integrity of NSSS. The NIMS is an integrated system thatmonitors the NSSS for loose parts at natural collection zones, coolant steam leakage and pipe crack propagation at potential leak and crack regions, vibration of the reactor core components(core support barrel, fuel assembly, etc.)and reactor coolant pump and motor vibration levels, pump shaft displacement(orbits) and RPM. This paper is intended to introduce the NIMS signal analysis methods using in nuclear power plant.
An online hybrid prognostics ANFIS-PF method with an application to gearbox for RUL prediction
Authors: Govahianjahromi Atefeh , Kim Jaehoon , Hamadache Moussa , Lee Dongik
Published: March 2024

Rotary components are dealing with performance degradation phenomenon, which contains the message of unexpected damages. Therefore, prognostics and health management (PHM), has been introduced to calculate andpredict the remaining useful life (RUL) in order to prevent costly damages or repairs. Data-driven, model-based and hybrid-based techniques are three main categories of PHM techniques. From the health monitoring view, the main idea is to use the experimental run-to-failure data as an intelligence-based model for our gearbox and predict the RUL with the probability (model) based method (Particle Filtering). Firstly, to perform our prognostics technique, we require the degradation information from gearbox. Therefore in duration of 10 days, we conduct a run-to-failure experiment for a test bench with initiative fault injection in day 7th. Period of last-three-days is considered as run-to-failure signal for proposed algorithm. After preprocessing the data, we apply a combined prediction method ANFIS-PF, using Adaptive Neuro Fuzzy Inference System (ANFIS) and Particle Filtering (PF). ANFIS used as a prediction model tool, while the particle filter method was used to find a step-ahead behavior of the gear. ANFIS as a powerful data-driven method will model the prediction of degradation data and finally this model isapplied to particle filtering to predict a-step-ahead of the gear behavior until failure will happen. Meanwhile, some important signal characteristics known as condition indicators (CIs) have been extracted from the residual, energy, frequency based data processing. Then, the energy-based health index (HI) is calculated using threshold and sum of distributions, to show the degradation trend of tested gearbox. The online prediction results properlydemonstrate the performances of the proposed ANFIS-PF algorithm, to predict the RUL of gearbox system with a 95% confidence boundary distribution.
System Component Degradation: Filter Clogging in a UAV Fuel
Authors: Skaf Zakwan , Eker Omer F. , Jennions Ian K.
Published: March 2024

The filtration of possible contaminant is an essential part of many engineering processes in industry. Clogging of the filtration medium is one of the primary failure modes in many application areas leading to reduced performance and efficiency. Imitation of real life clogging scenarios in laboratory conditions is not an easy task to perform, but is demonstrated here, with the profiles obtained being injected into a fuel system rig. This paper shows generic results from two benchmark rigs. One is a fuel system laboratory testbed representing an Unmanned Aerial Vehicle (UAV) fuel system and its associated electrical power supply, control system and sensing capabilities. It is specifically designed in order to replicate a number of component degradation faults with a high degree of accuracy and repeatability. The second is a purpose built filter clogging rig designed to givequality results to aid the development of prognostic algorithms. This paper’s contribution is to show results from the filter clogging rig and derive a transfer function, the relationship between filter clogging pressures and the fuelsystem valve openings, to enable the fuel system rig to operate as if the clogging filter were part of the system. The results show that the local pressure drop obtained from the fuel rig can be made to closely match the pressure drop levels from the filter clogging rig. This opens up examination of the effects of filter clogging on the full fuel rig system, providing data for future system prognostic work.
Comparing the parameter estimation methods of Weibull distribution with censored lifetime
Authors: Park Jihyun , Lee Juhyun , Ahn Suneung
Published: March 2024

Weibull distribution is widely used in reliability engineering and lifetime analysis because of its flexibility in modeling both increasing and decreasing failure rate. Weibull distribution has shape parameter and scale parameter, and it is difficult to estimate the parameters due to the no-closed form of likelihood function. In recent years, there has been studied on the approximating parameter estimation methods based on the simulation. In this study, we use the approximating parameter estimation of Weibull distribution with censored lifetime. The methods which are applied in numerical example are Bayesian estimation method, maximum likelihood estimation, and Markov chain Monte Carlo. Accuracy of estimation methods is performed by the mean square errors of parameter estimator in simulation reducing the experiment time. In addition, it can be helpful to set the design of experiment considering the characteristics of Weibull distribution with censored lifetime.
Fatigue Life Prediction Based on Walker and Masson Models
Authors: Zhou Jie , Huang Hong-Zhong , Zheng Bo , Peng Zhaochun
Published: March 2024

It is known that mean stress has significant effects on fatigue life prediction, and various modifications have been developed to explain the mean stress effect, yet seldom accounting for mean stress sensitivity. The Smith-Watson-Topper (SWT) model is one of the most widely used models that can give satisfactory predictions, and it is viewed as a particular case of Walker model when the material parameter γ = 0.5. The Walker equation takes both the mean stress effect and sensitivity into account and can give accurate predictions in many fatigue programs. In this paper, based on the Walker model and Masson model, a modified model accounting for both the mean stress effect and the mean stress sensitivity is proposed to estimate the fatigue life. Three sets of experimental data are used to validate the applicability of the proposed model. A comparison between the SWT model and Morrow model is also made. Theresults show that the proposed model has more accurate predictions than the others.
Towards a Physics Based Foundation for the Estimation of Bearings RUL
Authors: Gazizulin Dmitri , Klein Renata , Bortman Jacob
Published: March 2024

Rolling element bearing (REB) prognosis is the process of forecasting the remaining operational life, future condition, or probability of failure based on the acquired condition monitoring data. One of the common reasons for rolling element bearings failure is the rolling contact fatigue (RCF). Complete understanding of the fatigue process is critical for estimation of the bearing remaining useful life (RUL) and allows planning maintenance actions. In the current work, it is assumed that the spall generation, on the surface of the raceway, is a result of RCF. However, after the first spall formation, the bearing might be fully operational for millions of cycles. Thus, for the estimation of the bearing RUL it is also important to understand the damage propagation process. The proposed method of RUL prediction is separated into two steps: diagnostics and prognostics. The diagnostics includes characterization of the defect in terms of location, type, and extent. The prognostics includes estimation of the defect propagation as a function of time, using its characterization derived from the diagnostics step. It is expected that results of the current study will provide an estimation of the bearing’s RUL: from first spall formation to the unoperational bearing. The spall generation process, as a result of RCF, is modeled based on continuum damage mechanics with representation of material grain structure and implemented using a Finite Element software. The results of the model are in a good agreement with published theoretical and experimental data. The paper also includes a discussion on the ongoing research and the methodology that will be implemented as part of it.
Pilot study for the reliability evaluation of double suction volute pump
Authors: Jung Dong Soo , Jung Han Young , Kim Jong Cheol
Published: March 2024

In this paper, the study of test methods for the reliable assessment of the amount of suction pumps which bees root fluid is sucked into both around the impeller. Bee root amount suction pump is usually used for the purpose ofdischarging a large flow rate, such as water supply plant equipment and buildings. Centrifugal pumps are currently in production in the country is the trend in the exchange according to Chapter mode changes to improve the level of technology leaks due to wear of the mechanical seal from the existing impeller wear. When the mode is changed to give chapter and developed appropriate test method for evaluating the reliability of centrifugal pumps life testing time because the calculation method differs with these failure modes.
Performance Analysis of Single-Disc and Multi-Disc Check Valve
Authors: Park Tae-Kook , Lee Yong-Bum , Lee Jae-Ho , Kim Tae-Seok , Seo Dong-Jo
Published: March 2024

The check valve performs the function of protecting the pump from abnormal conditions in the piping system. The pressure variation caused by water hammer can be obtained using the Joukosky equation. The multi-disc check valve is closed by the weight of the main disc and the sub discs, so the bottleneck of the fluid outlets prevents water hammer. We reproduced the water hammer phenomenon in the constructed test equipment. The test confirmed that when the water hammer is applied to the check valve, the pressure of the multi-disc check valve rises less than that of the single-disc check valve. During the multiple-disc check valve test, there was no leakage in the leak test, thedifferential pressure was measured (0.02 ~ 0.18) bar in the pressure drop test, and the working pressure for the minimum working pressure test was measured at 0 bar.
Accelerated Life Test and Signal Monitoring of an Industrial Pressure Sensor
Authors: Shin Jung-Hun , Kim Jong-Cheol , Chang Mu-Seong , Lee Sik , Jung Dong-Soo , Cho Kum-Won
Published: March 2024

Sensing pressure is widely needed technique in the industry such as plant, vehicle, aerospace, and etc. Since each pressure sensor has its own ranges of operating conditions, but the manufacturers give quite large design margins. So although the sensors has own lifetimes, they are regarded as hardly failed products. This study developed the accelerated life test system of pressure sensors, and the failure monitoring and predicting module in the system was a differentiated tool developed using a data-driven algorithm. This module were expected to save the test time since specimens which do not fail yet can be regarded as failed ones. As a preliminary commitment, 8 specimens were used in the test bench. Although classical Weibull analysis and accelerated life test method can handle these censored data, prognostic judgments of the censored data can give another analysis result. The signal-based analysis in this study showed that mean and deviation values of pressure sensor signals did not represent the failure prediction metric and so researchers should devised other approaches to substitute the classical reliability analysis.
Mechanical Property Estimation for FDM 3D Printed Parts using Gaussian Process Regression
Authors: Kim Heechang , Park Seungtae , Park Eunju , Kim Namhun , Lee Seungchul
Published: March 2024

Recently, as the application of additive manufacturing products has increased, new demands for the manufacturing industry are increasing. FDM is one of the most popular methods of the additive manufacturing. The products which is printed by the FDM method can be used in various fields, but the mechanical property of the products is considered to be weaker than the conventional cutting or casting processing. Therefore, improvement of the mechanical properties of the FDM printing product is a factor that can contribute to the actual manufacturing industry. In this study, we propose an analysis on the improvement of mechanical properties of output products due to various experimental variables. The experimental variables include orientation, infill rate, and material. Also, we propose a statistical method to estimate the maximum tensile strength of the product which changes according to the variables before the printing of the product, and experimentally verify it to optimize product manufacturing, manufacturing time and material waste saving in the actual industrial field.We use the Gaussian process to estimate nonstationary mechanical properties with respect to infill rate which pioneers the estimation approach in specimenrelated experiments. By using GP, we come up with estimation values with its uncertainty which guarantees some extent of confidence interval for our estimation values.
Weight reduction of automobile wheel using topology optimization and 3-D printing production
Authors: Lee Dongoh , Choi Hyeonrok , Lee Younghun , Kim Namsu
Published: March 2024

Recently, environmental regulations for automobiles have been strengthened and the reduction of weight for automobiles is regarded as a crucial research area for solving the air pollution problem as well as saving the energy. In the past, the research for vehicle weight reduction was mainly focused on the material of the parts rather than the design optimization due to the limitations of the production process. However, more research has been recently carried out on the optimized design that overcomes the limitations of the production process by employing the additive manufacturing. In this study, the conventional wheel for automobile is redesigned through the topology optimization and it is manufactured using 3-D printer instead of casting. By redesigning the wheel, the equivalent mechanical stiffness can be maintained and the weight can be reduced. The reduction of weight for automobile parts achieved through the topology optimization and 3-D printing production technology has the potential to be utilized not only in the automobile industry but also in various industries such as portable device and the aerospace industry.
Requirements for Prognostics System to Improve Business Process of Machinery Maintenance Service
Authors: Hori Masakazu , Oyama Takuya , Imamura Makoto
Published: March 2024

The number of companies that provide "predictive maintenance service” is increasing. If we can detect the sign of machinery failure from the collected data by IoT technologies, we exchange the parts or repair them in advance. By predictive maintenance service, people in a plant get merits to avoid great losses by unexpected troubles such as a sudden machinery failure, a manufacturing line stop and so on. As for maintenance service providers, theyhave such merits that they can maintain a machinery systematically and provide maintenance service by a limited number and skill of maintenance engineers. In this paper, we firstly show the main processes for preventive maintenance service and predictive maintenance service, and then compare them and show the differences. In predictive maintenance, we have no regular manual inspection conducted in a preventive maintenance. We anticipate a maintenance task by the monitoring results of a machinery condition and make a plan for the task. To solve the issues concerning preventive maintenance service provision, we propose not only the requirements forbasic functionalities of prognostics system, but also the operational requirements for continuous service provision and the non-functional requirements to guarantee safe usage.
Correlation of Physics-of-Failure to Stress-strain Curve for Quickturn Monitoring of Solder Joint Reliability
Authors: SA Yoonki
Published: March 2024

Physics-of-failure based reliability prediction technology is one of the key technologies in the field of prognostic and health management (PHM). In this study, physics of failure in solder interconnection is correlated with stress-strain curve obtained by die pull test to predict the joint quality and monitor the property change of solder bump within a reduced time period. Ball-gridarray (BGA) Si chip containing 6,000 solder bumps is used to demonstrate the validity of proposed method. With the conventional method using SEM analysis, it is very time-consuming to observe every single micro-bump and define each failure mode especially when IC includes a great number of solder bumps. The results indicate that it is possible to establish quick-turn monitoring methodology for the solder interconnection reliability and property change from the stress-strain curve of solder joints. Baseline strain-rate for ductile to brittle transition for reference joint of Sn-0.7Cu solder composition is investigated so that all the other deviations from the reference can be captured by using stress-strain relationship. In addition, it is found that failure mode in solder joint is correlated with stress-strain plot and therefore, joint reliability can be predicted without identifying the failure mode of the joints through SEM inspection. This study can provide the guideline for solder joint PHM from solder bump to chip system level.
A Study of the Degradation of Electronic Speed Controllers for Brushless DC Motors
Authors: Gorospe Jr. George E. , Kulkarni Chetan S. , Hogge Edward , Hsu Andrew , Ownby Natalie
Published: March 2024

Brushless DC motors are frequently used in electric aircraft and other direct drive applications. As these motors are not actually direct current machines but synchronous alternating current machines; they are electronically commutated by a power inverter. The power inverter for brushless DC motors typically used in small scale UAVs is a semiconductor based electronic commutator that is external to the motor and is referred to as an electronic speed control (ESC). This paper examines the performance changes of a UAV electric propulsion system resulting from ESC degradation. ESC performance is evaluated in simulation and on a new developed testbed featuring propulsion components from a reference UAV. An increase in the rise/fall times of the switched voltages is expected to cause timing issues at high motor speeds. This study paves the way for further development of diagnosticand prognostic methods for inverter circuits which are part of the overall electric UAV system.
Assembly Quality Diagnosis of Planetary Gear Sets
Authors: Han Soonyoung , Huh Jiung , Choi Hae-Jin
Published: March 2024

Product quality is one of the most important factors to be considered in manufacturing industries. Autonomous production line rapidly increases its productivity; however, quality check becomes a difficult problem and a smart system for quality assurance is indispensable in the modern production line In this study, we developed a transmission error based machine learning algorithm to check the quality of planetary gear assemblies and identify the defective parts in the planetary gear sets.
Development of Field-Applicable Health Monitoring Method for Photovoltaic Module Array
Authors: Lee Hyeonseok , Oh Wonwook , Han Changwoon
Published: March 2024

Solar cell modules are connected in serial and parallel in a photovoltaic system. All the solar cell modules degrade over time and the most degraded module in a string decide the output level of the string. In this study, we suggest a heath monitoring methods which enable to detect the most degraded module in a string without separating the module from string. We suggest placing a non-transparent film on a module in a string to make an artificial shading effect and monitoring the current-voltage curve of the string as placing the film to the next one. We show analytically that the most degradation module can be detected by comparing all the string current-voltage curves. We demonstrate the method for a field photovoltaic string.
Predictive Model of an Elastomer for Dynamic Properties under Thermal Environment
Authors: Lee Dooho
Published: March 2024

In this paper, the variability of dynamic stiffness in an elastomer due to thermal aging is estimated using a degradation model and uncertainty quantification method. A degradation model introduced for the thermal aging also describes the variation of the material properties along time.A finite element model describes the dynamic characteristics of the elastomer. The variability of the predicted degradation under the quantified uncertainties is estimated using the double-loop eigenvector dimension reduction (EDR) method. The proposed method is applied to a mount as a numerical example.
Impact Localization on Composite Wing Using a Single FBG Sensor and Error Outlier Based Impact Localization Algorithm
Authors: Shrestha Pratik , Park Yurim , Kwon Hyunseok , Kim Chun-Gon
Published: March 2024

Low velocity impact on composite structure can result in occurrence of barely visible impact damage (BVID) which are difficult to detect. Aircraft structures are vulnerable to such BVID because of low velocity impact due to runway debris, bird strike, tool drop, etc. Therefore, low velocity impact monitoring of composite structure is highly desirable for impact detection and localization. Additionally, SHM of large-scale structures with fewer numbers of sensors is desirable to reduce the complexity involved with the dataacquisition and processing. In this paper, feasibility of localizing low velocity impact on composite structure using a single sensor was investigated. Three FBG sensors were attached on the surface of the full-scale  composite wing and the signal from each of the FBG sensor was processed using the error outlier based impact localization algorithm to predict the location of the impact. The localization results demonstrated the feasibility of localizing impact on composite structure using a single FBG sensor; overall, the impacts were localized with average error of about 26.6 mm.
Framework for a Uniform Description of Prognostics and Health Management
Authors: Henß Mark , Zeiler Peter , Bertsche Bernd
Published: March 2024

The successful application of Prognostics and Health Management (PHM) systems is increasing steadily worldwide. One reason for this is the increasing number of smart products entering the market. Mirroring smart products, PHM systems are now developed and applied in avariety of engineering disciplines, all using different models and methods. Model, method, and product type diversity all lead to highly complex systems. To handle the complexity in an efficient way, this paper introduces a common base for PHM systems, in the form of a framework – the goal being a generic model with clear formalization of both notation and semantics. Accordingly, the PHM System is separated into both a RUL-Health model and a context model. Both are described and connected through a roles and relation model of their modules. Diagnosis and prognosis modules – estimating the components’ health using lifetime models – are RUL-Health based. For a holistic description, the general lifetime model (GLM) is introduced. This allows different measures of component health to be represented in a single model and reduces the complexity to two metrics – remaining useful life (RUL) and health index (HI). These metrics – combined with internal/external requirements & targets – arethe input for the context modules’ optimization and decision making.
A Metamaterial-Based Energy Harvester Design for Enhanced Power Supply to Wireless Sensor Network
Authors: Shin Yong Chang , Yoon Heonjun , Jo Soo-Ho , Yi Guilian , Kim Miso , Park Choon-Su , Choi Wonjae , Youn Byeng D.
Published: March 2024

Health sensing module in prognostics and health management (PHM) requires accurate acquisition of sensory signal and optimal deployment of sensor nodes. To this end, wireless sensor network (WSN) is widely utilized by virtue of the ease of the sensor deployments and the advances in the wireless communication. However, periodic replacements of the batteries are demanded for continuous operation of the WSN, which incurs additional costs and temporary suspension. To overcome these drawbacks, vibration energy harvesting (VEH) has emerged as a possible solution to realize self-powered/sustainable WSN operation. The VEH converts ambient vibration into electric power through an energy conversion medium. Low output power density, however, is still a critical issue in the VEH for its feasible application. Metamaterial-based energy harvesting (MBEH) has recently been proposed as a breakthrough technology to drastically improve output power generation. Metamaterials are engineered structures to exhibit exotic properties such as a bandgap. The bandgap refers to a certain frequency range within which elastic waves are prohibited to propagate. It can be used to enlarge elastic wave energy going into the energy conversion medium. While various mechanism for the MBEH has been introduced, only a few research has considered detail factors that can degrade the metamaterial’s performance. In this study, we propose a new phononic crystal design which prevents wave cancellation to improve the metamaterial’s performance. In building the model, the multiphysics finite element method are used to analyze dispersion and energy harvesting performance. Finally, the energy harvesting performance of the proposed design is compared with the design without the consideration of the wave cancellation.
Fault diagnosis of tilting-pad type fluid film bearings in high-speed rotating machinery
Authors: Sun Kyung Ho , Kang Dong Hyuk , Kim Byeong Ock , Lee Jai-Kyung , Lee Donghyun
Published: March 2024

Of all fluid film bearing types, tilting pad bearings offer the optimal rotordynamic stability due to their excellent stiffness and damping characteristics. Due to the reduction in cross-coupling stiffness, tilting pad bearings are ideal for use in high-speed and high-load turbomachineryapplications, such as industrial compressors and steam/gas turbines. In this investigation, we propose the method to diagnose the main failure mode of tilting pad journal bearings such as babbitt metal wear and pivot wear in bearing pads. Various experimental cases for bearing wearwere precisely implemented through a high-speed rotating test rig operating at 10,200 rpm. The vibration and temperature data were obtained through a condition monitoring system and various features from shaft vibration, bearing metal and lubrication temperature data were extracted for the fault diagnosis. In this study, we have shown that even defects due to the abrasive wear of fluid film bearings including general rotor faults, such as unbalance, misalignment and rubbing, can be accurately diagnosed.
Feature Selection Method for Life Prediction in Multiple Degradation Unit: Generalized Rank Mutual Information
Authors: Hwang Taewan , Kim Keunsu , Kim Su J. , Jeon Byungjoo , Youn Byeng D.
Published: March 2024

Many industries are making efforts to minimize the losses caused by shutdown of manufacturing facilities and to set an optimal maintenance schedule. In this context, prognostics, which predict remaining useful life (RUL) based on information extracted from sensory signals, have attractedattention. There are three methods to perform life prediction: physics-based, data-based, and hybrid. However, data-driven methods are the only way to apply them to a complex industrial facility. By assuming multiple degradation unit data, we can extract various features from the data and select the best feature to create a health index(HI). In this study, we propose a new method for the feature selection step that greatly determines the performance of RUL prediction. Proposed algorithm can automatically select features that are monotonic and have a consistent level of value in normal and failure zone. We validate our method using real degradation data acquired from bearing life testbed.
Tooth-wise Fault Identification for a Planetary Gearbox Based on a Health Data Map
Authors: Ha Jong M. , Park Jungho , Youn Byeng D.
Published: March 2024

Vibration-based fault diagnostics of a planetary gearbox is known to be challenging due to revolving planet gears. Signal transfer path between the planet gear of interest and the vibration sensor is periodically varying, and thus the vibration signal is modulated. To detect the faults in the planet gear, vibration signals are extracted using a window function when the signal transfer path is minimized. The extracted signals are transferred to the tooth domain of the planet gear of interest for further analysis. However, due to various uncertainties such as manufacturing and assembly tolerances that can significantly affect the vibration characteristics, vibration signals could have an unexpected modulation characteristics regardless of the signal transfer path. It means that the features from the faulty tooth of the gear can be discarded by the abuse of the window function during the extraction procedure. To overcome these challenges, this paper proposes an original idea of a tooth-wise fault identificationfor a planetary gearbox based on a health data map. In doing so, vibration signals are processed with time synchronous averaging (TSA) and auto-regressive minimum entropy deconvolution (AR-MED) filter that don’t require the use of the window function. Vibration signals processed in time-domain must be aligned in the domains of a pair of gear teeth (i.e. ringplanet gear teeth pairs). Two-dimensional health data map can sketch the health data corresponding to every pairs of gear teeth to isolate the location of the faulty gear tooth. For demonstration of the proposed method, this paper presents case studies using an analytical model and a gearbox testbed.
A Solenoid Valve Multi-physics Model for Railway Braking System Diagnosis: Development and Validation
Authors: Jo Soo-Ho , Lee Dongki , Youn Byeng D. , Oh Hyunseok
Published: March 2024

Solenoid valves are an electromagnetically-operated valve that can be used to control the movement of air in railway braking systems. To develop a physics-based diagnostic model for fault detection, it is critical to build a valid model that describes the behavior of solenoid valves accurately. This study presents an experimentally-validated multiphysics analytical model to predict solenoid valve behaviors. The activation of solenoid valves is associated with multi-physics mechanisms that are coupled together. Electromagnetic, fluidic, and mechanical mechanisms are modeled with ordinary differential equations. The results from the multi-physics analytical models are validated with those from the experiments. The undefined parameters of the model are statistically calibrated. The proposed model can be promising for solenoid valve diagnostics in two aspects. First, thecomputational cost of the analytical model is extremely low in comparison with that of a commercial finite element method (FEM). Second, the model provides an insight regarding explicit relationship between the inputs and outputs of solenoid valves.
Equivalent Accelerated Degradation Test Plans in a Nonlinear Random Coefficients Models
Authors: Kim Seong-joon , Bae Suk Joo
Published: March 2024

Design of optimal Accelerated Degradation Testing (ADT) plan has been extensively researched over several decades. In practice, due to the rapidly changing development and assessment environment, pre-established plans often fail to meet reality. Therefore, designing a test plan that is equivalent to the target plan using an different stress-loading or a testing condition is needed to allow for more flexibility. However, there exists currently little work in the development of equivalent ADT plan. In this paper, we proposes an equivalent cost-effective accelerated degradation test (ADT) plan in the context of a nonlinear random-coefficients model. The proposed model is applied to a well-known constant-stress ADT problem in the literature.
On-Line Parameter and RUL Updating for Degradation Processes with Three-Source Variability
Authors: Peng Weiwen , Chen Yuefeng , Yang Yuan-Jian
Published: March 2024

Degradation model based remaining useful life (RUL) prediction is often used in prognostics and health management (PHM). In practice, three-source of variability, i.e., the temporal variability, unit-to-unit variability, and measurement variability, is often encountered in degradation modeling, leading to complex degradation models and great challenge for the parameter estimation and RUL prediction. Commonly, off-line methods are used, which, however, cannot fulfill the real-time requirement of decision-making in the PHM. In this extended abstract, a generic degradation model is introduced, which can characterize the three-source variability and provide a flexible for on-line parameter and RUL updating. An integrated simulation-based filtering method is introduced by feeding the output of a Markov chain Monte Carlo simulation into an extended particle filter, which can fuse the historical trajectories and condition monitoring observations and update the parameter and RUL simultaneously. Critical aspects of the generic model and the filtering method are presented.
Pengembangan Tampilan Website Profile Kesehatan Sewon di Dinas Kesehatan Bantul
Authors: Tyas Zahra Arwananing , Faruqi Almando Yusuf Hasda
Published: March 2024

Perkembangan era digital memberikan dampak yang bermanfaat dalam dalam masyarakat. Dengan berkembangnya Teknologi Informasi di era sekarang hampir seluruh informasi mengenai kesehatan pada Kabupaten Bantul berupa online. Informasi kesehatan online ini berbentuk halaman website, Tampilan website dinkes Bantul belum menarik ketika masyarakat mengaksesnya dan tampilan isi konten yang begitu banyak tulisan membuat masyarakat malas untuk membaca isi web. Tujuan penelitian ini untuk memperbarui tampilan dan isi halaman website. Metode yang dilakukan saat penelitian meliputi tahapan wawancara dan analisis dokumen. Dari penelitian yang dilakukan telah dibuat tampilan website berisi informasi kesehatan yang menghasilkan 6 halaman yang dimana setiap informasinya di buat menarik dengan tampilan modern.
Aplikasi Presensi Karyawan Menggunakan Geolocation Dan Metode Haversine Berbasis Android
Authors: Crisna Muhamad Ade
Published: March 2024

Kehadiran karyawan memiliki peranan yang penting agar proses atau kegiatan berjalan dengan baik, Universitas Catur Insan Cendekia dalam kegiatan presensi karyawannya sudah menggunakan mesin fingerprint, tetapi belum terhubung secara sistem, sehingga Bagian Kepegawaian harus mengambil data secara manual setiap bulannya, dan dalam pengolahan rekap data presensi masih menghitung jumlah presensi setiap karyawan secara manual dan juga dalam proses karyawan melakukan izin masih dengan cara manual, dengan menghubungi Bagian Kepegawaian melalui aplikasi WhatsApp untuk melakukan konfirmasi izin kerja. Maka diperlukan sebuah aplikasi presensi online yang dapat terhubung langsung kepada Bagian Kepegawaian serta dapat melakukan pengolahan rekap data presensi secara otomatis, dan juga dapat memberikan kemudahan kepada karyawan dalam melakukan presensi dan juga izin kerja. Geolocation digunakan untuk mendapatkan koodinat lokasi dari sebuah perangkat smartphone dan Haversine Formula merupakan sebuah metode yang digunakan untuk menghitung jarak yang digunakan pada penelitian ini, sehingga karyawan hanya dapat melakukan presensi di lingkungan Universitas Catur Insan Cendekia, dan memblokir penggunaan aplikasi fake global positioning system.
Evaluation of Fibronectin, C-Reactive Protein and Lipid Profile in Patients with COVID-19
Authors: Hameed Nagham I. Abdul , Aldoori Zina A. A.
Published: March 2024

The SARS-CoV-2 coronavirus was the initial cause of the worldwide epidemic of coronavirus disease 2019 (COVID-19), which started in March of 2020. Patients with coronavirus disease (COVID-19) may have elevated C-reactive protein (CRP) and fibronectin levels owing to the inflammatory response caused by the virus. Nonetheless, the available data imply that infection with COVID-19 affects lipid profile and leads to dyslipidemia. Seventy samples were collected from the Sanitary Isolation Unit of Medical City Hospital in Kirkuk Governorate for this study. Forty of them were taken from SARS-COV2 patients, whereas the other thirty were from healthy individuals. The ages of the two groups ranged between (30-65) years. When a real-time polymerase chain reaction (PCR) tested positive, patients had blood collected to check for other biochemical and inflammatory markers. Fibronectin, C-reactive protein, and d-dimer were some of the indicators checked. The patients' blood was also analyzed for lipids such as total cholesterol (T.C), triacylglyceride (TG), Low-Density Lipoprotein Cholesterol, and High-Density Lipoprotein Cholesterol (HDL-C). In this study, researchers discovered abnormally high concentrations of CRP, D-dimer, fibronectin, triglycerides, LDL-C, and VLDL. The patients also had considerably lower levels of T.C and HDL-C compared to the controls.
Analysis of the Relationship between Neutrophils, Lymphocytes, and Comorbidities with Time to Death in COVID-19 Patients
Authors: Tanuwijaya Hendra , Djajalaksana Susanthy , Tantular Rezki
Published: March 2024

BACKGROUND: COVID-19 caused 5.4 million deaths in 2021. Various parameters such as comorbidities and laboratory findings are known to be predictors of death in COVID-19, but these findings differ in each country, and their relationship to time of death has not been widely studied. This study was conducted to determine the relationship between levels of neutrophils, lymphocytes, neutrophil to lymphocyte ratio (NLR), and comorbidities with time to death in COVID-19.
METHODS: A retrospective cohort study was conducted from April 2020 to September 2021 in the COVID-19 Inpatient Room at RSUD dr. Saiful Anwar Malang with 300 COVID-19 patient subjects aged 18 years and over. Data on comorbidities (hypertension, CAD, HF, obesity, COPD, CKD, cancer), time to death, and laboratory were taken from medical records. The Kolmogorov-Smirnov test, Chi square test and Mann Whitney test were carried out to analyze the data.
RESULTS: There was a significant relationship between neutrophil levels and time to death, as well as a significant difference in neutrophil levels in the group with time to death >48 hours with comorbidities compared to <48 hours with and without comorbidities. There was a significant difference in lymphocyte levels and NLR in patients with comorbidities with time to death <48 hours and >48 hours (p < 0.05).
CONCLUSION: There is a significant relationship between neutrophil levels and time to death, both in subjects with and without comorbidities showing the potential of neutrophil levels as a predictor of time to death in COVID-19.
Perspectivas atuais sobre Síndrome dos Ovários Policísticos: abordagens diagnósticas e terapêuticas
Authors: Vytor Cardoso Nobre Paulo , De Araújo Alves Júnior  Josivaldo , Baracho Macena Leopoldo , Saleme Moreira Vasconcelos da Rocha Manuela , Erika Neumann Costa Katherine , Raquel Batista Melo Thereza , Silvina Rodrigues Silva Lara , Toledo Calheiros Nobre Sampaio Mariana , Victor Santos Meneses João , Inês Costa Cordeiro Karine , César Guimarães Bancilon Carlos , Silveira da Silva Liliane
Published: March 2024

Introdução: A síndrome do ovário policístico (SOP) é uma condição endócrina complexa caracterizada por disfunção ovariana e hipersecreção de andrógenos. Objetivo: Avaliar as bases fisiopatológicas e tratamentos associados à síndrome do ovário policístico. Metodologia: Trata-se de uma revisão bibliográfica que incluiu artigos originais e revisões sistemáticas em inglês e português, que abordaram aspectos fisiopatológicos e manejo SOP, publicados entre 2016 e 2024, selecionados nas bases de dados PubMed, Scopus e SciELO. Após a seleção criteriosa, foram escolhidos 18 artigos para compor esta revisão bibliográfica. Resultados: A SOP é influenciada por mecanismos epigenéticos, exposição a compostos tóxicos, hiperinsulinemia e hiperandrogenismo, que afetam diretamente a ovulação e metabolismo. A obesidade também contribui para a SOP. O tratamento envolve mudanças no estilo de vida e medicamentos para controlar sintomas e restaurar ovulação. Considerações: A SOP é uma condição complexa, envolvendo fatores genéticos, epigenéticos, ambientais e metabólicos. O tratamento visa abordar sintomas e mecanismos subjacentes, com mudanças no estilo de vida e terapia farmacológica individualizada. Uma abordagem multidisciplinar é essencial para melhorar a qualidade de vida dos pacientes.
Development of modification process of casting steels by fine mechanical engineering wastes
Authors: Kidalov A.N. , Grebnev D.Yu. , Gabel'chenko N.I. , Grebnev Yu.V.
Published: March 2024

The work proposes to use finely dispersed waste of alloy steels as modifying additives to create additional crystallization centers in the steel melt, which makes it possible to increase the level of mechanical properties, in particular impact strength in 20G1FL, 45FL, 110G13L steels.
A review of drivers and barriers of Digital Twin adoption in building project development processes
Authors: Jahangir Muhammad Farhan , Schultz Carl Peter Leslie , Kamari Aliakbar
Published: March 2024

Over the past few years, the AECO Industry has undergone a shift toward digital transformation, with a growing trend towards adopting innovative technologies such as Digital Twin (DT). DT offers a wide range of applications throughout the building development process. However, some specific factors impede its widespread adoption in the building industry. This study aims to systematically review the available literature on the building project development process from the perspective of DT, with a particular focus on predictive simulations, i.e., co-sims. The review provides a comprehensive overview of drivers and barriers to DT adoption through an analysis of 147 studies between 2013 and 2023. The research identifies seven external and 41 internal drivers, including efficient project management and monitoring, predictive maintenance, and the collection and visualization of real-time data, all of which contribute to improved decision-making processes and reduced operational expenses. Further, the study identifies nine external and 31 internal barriers that impede the adoption of DT in the building development process. These barriers encompass challenges such as a high initial investment cost, a scarcity of a skilled workforce, difficulties in data interoperability, and resistance to change within the organization. A key outcome of the literature review is having identified the opportunity to exploit technologies developed in the automotive sector that enable a seamless integration of specialized simulator models in building development processes, resulting in collaborative simulations. Thus, we propose the concept of a Building Simulation Identity Card (BSIC) to be pursued in future research that would enable stakeholders to address the challenges of collaboration, cooperation, coordination, and communication by creating a common vocabulary to effectively facilitate the adoption of DT in the building's development process.
Percepción de la salubridad de los macerados de Pisco en el distrito de Barranco: Un estudio sobre la evaluación de los consumidores
Authors: Tejeda-Rodríguez Isaac , Pizarro-Ramos Pedro
Published: March 2024

El estudio se realizó en el distrito de Barranco, reconocido por su relevancia en la oferta y consumo de macerados de Pisco. Los bares fueron seleccionados al azar siguiendo el Plan de Desarrollo Turístico Local de Barranco 2020, respaldado por la "Ordenanza Nº 554-2020-MDB". Se utilizó un cuestionario de 36 preguntas cerradas en Google Forms, validado por tres expertos, administrado en dos sesiones y analizado en MS Excel. La encuesta se llevó a cabo en el boulevard de Barranco durante cuatro fines de semana, entrevistando a clientes de bares locales los sábados, con el objetivo de comprender las percepciones de los consumidores habituales de macerados de Pisco. La muestra incluyó 341 personas seleccionadas al azar y se centró en percepciones visuales, gustativas y olfativas relacionadas con la experiencia de consumo de Pisco. Los resultados revelaron que la percepción visual, gustativa y olfativa influyen en la elección de un consumo saludable de Pisco, siendo el precio y el color factores determinantes. Aunque el sabor fue crucial, la percepción olfativa no fue un indicador confiable de autenticidad. Estos hallazgos resaltan la importancia de educar a los consumidores sobre la identificación de Pisco de calidad y considerar aspectos visuales, gustativos y olfativos en la selección de bebidas alcohólicas para mantener la confianza del consumidor.