Earticle

The purpose of this study is to evaluate of habitability on buildings under machine induced vibration. In order to evaluate of habitability, we have measured the accelerations at six different points in a machine room which is located at Kumoh Institute of Technology. This measurement values were analysed as velocity, displacement by numerical integral. After that, the results were applied to foreign building code. This study can be used for basic evaluating method of machine induced vibration.

For sustainable forest management and the circular use of forest resources, it is essential not only to undertake afforestation and silviculture but also to perform timber-harvesting operations within a safe working environment. In Korea, the number of workers engaged in timber harvesting has been declining and aging, prompting active research into the mechanization of harvesting systems to enhance productivity. In particular, conventional chainsaw-based felling and forwarding operations are gradually being replaced by excavator-based processes. However, excavators operated in rough terrain during felling and forwarding operators to elevated whole-body vibration, which may precipitate musculoskeletal disorders and ultimately degrade work performance. Currently, research investigating operators’ whole‐body vibration exposure during individual task elements of excavator‐based forestry machinery remains scarce. Therefore, in this study we categorize the vibrations experienced by excavator operators during felling and forwarding into discrete sub-tasks and analyze their dynamic characteristics using parameters such as crest factor, maximum vibration amplitude, cumulative vibration exposure, and root-mean-square (RMS) acceleration. Finally, by applying the calculated TWRMS and TVDV values cumulative exposures to the health-guidance risk levels and comfort-response criteria defined in ISO 2631-1, we quantitatively evaluate the whole-body vibration exposure of operators performing excavator-based harvesting and forwarding operations. Excavator-based harvesting and forwarding tasks exhibited TWRMS and TVDV values exceeding ISO 2631-1 health thresholds during 4–8 hours of operation, inducing comfort ratings from “uncomfortable” to “very uncomfortable.” Forwarding showed higher exposure than felling, and attachments alone provided insufficient mitigation. Future research should explore remote-controlled harvesting to reduce operator vibration exposure.

To keep an enterprise's competitiveness on the condition of the automatic manufacturing system such as FA, FMS and CIM, all the maintenance problems should be considered seriously in not only in production and maintenance but also in related industrial productivity. This paper presents the study of equipment reliability improvement for rotary-machine vibration analysis. Based on these analyses, the maintenance management information system, and the machinery condition diagnosis technique are studied by using of the real-time diagnostic. Therefore, it is expected to improve the efficiency of business processes in the production and safety when we use this system.

The machine tool has been widely applied to manufacture and trial production in many machinery industry. Blast induced ground vibration may cause an environmental impact such as damage on adjacent structures and facilities. In order to evaluate the influence of blast induced vibration on the machine tools, we measured the blast vibration and vibration of machine tools. Based on the evaluation, we suggest that the influence of the blast vibration on the accuracy of machine tools.

본 논문에서는 진동 신호에 대한 2차원 변환을 이용하여 신뢰성이 높은 유도 전동기의 결함을 검출하기 위한 방법을 제안한다. 이를 위해 먼저 시간 영역 샘플을 0에서 255범위로 정규화 함으로써 1차원 진동신호를 2차원 회색조 영상(gray image)로 변환한다. 다음으로 질감 특징을 갖는 이러한 결과 영상으로부터 DNS(dominant neighborhood structure) 지도(map)를 생성하고 이로부터 전역 영상 특징(global image feature)을 추출한다. 이러한 질감 특징은 유도 전동기의 결함 분류를 위해 one-against-all(OAA) 다중 클래스 서포트 벡터 머신(multi-class support vector machines)의 입력으로 사용된다. 본 논문에서는 제안하는 방법의 성능을 평가하기 위해 기존의 결함 검출 알고리즘과의 분류 정확도를 비교한다. 또한, 진동 신호 취득 시 포함될 수 있는 잡음에 대한 강인성을 평가하기 위해 취득한 진동 신호에 백색 가우시안 잡음(white Gaussian noise)을 추가하여 분류 정확도를 비교한다. 실험 결과, 제안한 방법은 기존의 알고리즘들보다 잡음이 포함되지 않은 환경과 잡음이 포함된 환경 모두에서 높은 분류 정확도를 보였다.

This paper proposes an approach using a two-dimensional representation of vibration signals for the highly reliable fault classification of induction motors. To deal with this issue, this paper first converts one-dimensional vibration signals to two-dimensional gray-level images by normalizing time-domain samples in the range from 0 to 255. Using the resulting images, which have texture characteristics, this paper extracts features by generating dominant neighborhood structure maps which are used to extract the global image features. The texture features are then used as inputs of one-against-all multi-class support vector machines to identify faults in the induction motors. To evaluate the performance of the proposed approach, we compare it with two conventional state-of-the-art algorithms in terms of classification accuracy. In addition, this paper explores the robustness of the proposed approach in a noisy environment by adding white Gaussian noise to the acquired vibration signals. Experimental results indicate that the proposed approach outperforms conventional algorithms in terms of classification accuracy. Moreover, the proposed approach achieves higher classification accuracy even in a noisy environment.

The excessive vibration of motor support in container crane’s machine room affects lifespan of the structure and driving comfort of the operators. According to the result of the vibration site test, the main causes of the machine room vibration in container crane were found out. In order to reduce the vibration response, based on simplifying the system as a single degree of freedom vibration system model and finite element method, two kinds of rapid vibration analysis software were compiled respectively by using Visual Basic (VB), which can parameterize the motor and its support. Meanwhile, their advantages and disadvantages were compared. By analyzing the effects of different motor support structure layouts on its natural frequency and maximum vibration velocity response, it is concluded that increasing and improving the motor support’s cross-sectional moment of inertia and lateral stiffness can reduce the system vibration response. The rapid analysis software’s computing results are almost the same as ANSYS computing results. Furthermore, motor support structure of the container crane machine room was optimized through the original software. The accordance of vibration test data and analysis results prove the credibility and the reliability of the rapid analysis software and methods.

본 연구는 지하철 운행이 인접구조물의 소음 진동에 미치는 영향을 분석한 연구로서, 도시철도에 인접한 구조 물이 지하철 운행에 따른 소음 진동에 영향을 미치는 주요 매개변수를 도출하였다. 연구 대상은 도시철도의 대표적인 지하철 구조물인 박스와 터널 구조물이며, 수치해석 결과의 상관관계를 분석하고 머신러닝 기법을 적용하여 인접구조 물에 미치는 소음 진동 영향 범위를 도출하였다. 이를 통해 기존 수치해석 결과와 머신러닝 기법 적용의 적정성을 비 교 분석하였다. 연구 결과, 도시철도 깊이, 구조물 굴착 깊이 및 지하수위의 상대 깊이 차이가 인접구조물의 소음 진 동에 영향을 미치는 주요 매개변수로 분석되었으며, 그 중 도시철도 깊이와 구조물 굴착 깊이 간의 차이가 가장 큰 영향을 미치는 매개변수로 도출되었다. 또한, 본 연구에서 제시한 머신러닝 알고리즘을 바탕으로 지하철 운행으로 인 한 소음 진동 예측이 가능함을 확인하였다. 따라서 본 연구에서 제안한 예측 기법을 통해 기존의 수치해석에 의존했 던 소음 진동 영향 평가를 간편하게 수행할 수 있으며, 이를 바탕으로 민원 발생 가능성을 사전에 파악할 수 있을 것 으로 판단된다.

This study is a study on the effect of subway operation on noise and vibration of adjacent structures, and the main parameters affecting the noise and vibration of adjacent structures constructed adjacent to urban railways due to subway operation were derived. The correlation of the numerical analysis results was analyzed, and the machine learning technique was applied to derive the range of noise and vibration effects of subway operation on adjacent structures. The appropriateness of applying the machine learning technique was analyzed by comparing it with the existing numerical analysis results. Based on this, this study proposes a machine learning analysis technique that can predict the subway noise and vibration range based on the field conditions of excavation work adjacent to urban railway lines.

This paper describes a new digital signal processor (DSP) imbalance measurement system dedicated to real-time vibration analysis on rotating machine. To accomplish real-time analysis, the vibration signals are on-line acquired and processed to analyze the mass imbalance and phase position. This is achieved through the use of FFT and Lissajous diagram. The method followed to analyze the mass imbalance with the chosen hardware and software solutions are described in detail in this paper. Several experimental tests demonstrate the efficiency and accuracy in imbalance analysis performance of the DSP system.

Among many dimensional and dimensionless amplitude parameters, kurtosis (4-th normalized moment of probability density function) is generally regarded as a sensitive good parameter for machine diagnosis. However, higher order moment may be supposed to be much more sensitive. Bicoherence is an absolute deterioration factor whose range is 1 to 0. The theoretical value of n-th moment divided by n-th moment calculated by measured data would behave in the same way. We propose a simplified calculation method for an absolute index of n-th moment and name this as simplified absolute index of n-th moment. Some favorable results are obtained.

The fractional slot windings are widely used in rotating machine in order to increase the flexibility of design and improve the voltage waveform. However, the MMF wave of fractional-slot windings are found to contain unique harmonic component, which are designated as even order space flux harmonics, fractional number flux harmonics, or both. They may cause stray loss and stator core vibration. This paper proposes new winding methods "novel interspersed windings" and "expanded group windings" to reduce these harmonics. The advantages of two proposed windings are verified by using numerical analysis and measurement test of winding model.

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in three-phase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.

Background: This study aimed to assess the whole-body vibration (WBV) exposure among large blast hole drill machine operators with regard to the International Organization for Standardization (ISO) recommended threshold values and its association with machine- and rock-related factors and workers' individual characteristics. Methods: The study population included 28 drill machine operators who had worked in four opencast iron ore mines in eastern India. The study protocol comprised the following: measurements of WBV exposure [frequency weighted root mean square (RMS) acceleration ($m/s^2$)], machine-related data (manufacturer of machine, age of machine, seat height, thickness, and rest height) collected from mine management offices, measurements of rock hardness, uniaxial compressive strength and density, and workers' characteristics via face-to-face interviews. Results: More than 90% of the operators were exposed to a higher level WBV than the ISO upper limit and only 3.6% between the lower and upper limits, mainly in the vertical axis. Bivariate correlations revealed that potential predictors of total WBV exposure were: machine manufacturer (r = 0.453, p = 0.015), age of drill (r = 0.533, p = 0.003), and hardness of rock (r = 0.561, p = 0.002). The stepwise multiple regression model revealed that the potential predictors are age of operator (regression coefficient ${\beta}=-0.052$, standard error SE = 0.023), manufacturer (${\beta}=1.093$, SE = 0.227), rock hardness (${\beta}=0.045$, SE = 0.018), uniaxial compressive strength (${\beta}=0.027$, SE = 0.009), and density (${\beta}=-1.135$, SE = 0.235). Conclusion: Prevention should include using appropriate machines to handle rock hardness, rock uniaxial compressive strength and density, and seat improvement using ergonomic approaches such as including a suspension system.

This paper presents a comparative study on motor characteristics with specific consideration of radial vibration force in interior permanent magnet synchronous motors (IPMSM) according to pole/slot combinations. Three IPMSM models, 16-pole/15-slot design, 16-pole/18-slot design and 16-pole/24-slot design are built, in which 16-pole/15-slot and 16-pole/18-slot designs provide high winding factor and 16-pole/24-slot design is known as a general pole/slot combination. By coupling finite element analysis (FEA) with equivalent circuit method, motor characteristics, back electro-motive force (Back-EMF), inductances, cogging torque, etc. as well as machine output performances are analyzed and compared. The radial vibration force (RVF) distribution in air gap causing stator vibration and noise is interested. It is expected that this study help with appropriate choice of pole/slot combination in IPMSM design.

Fine blanking is a press processing technology that can process most of the product thickness into a smooth surface with a single stroke. In this fine blanking process, shear is an essential step. The punches and dies used in the shear are subjected to impacts of tens to hundreds of gravitational accelerations, depending on the type and thickness of the material. Therefore, among the components of the fine blanking mold (dies), punches and dies are the parts with the shortest lifespan. In the actual production site, various types of tool damage occur such as wear of the tool as well as sudden punch breakage. In this study, machine learning algorithms were used to predict these problems in advance. The dataset used in this paper consisted of the signal of the vibration sensor installed in the tool and the measured burr size (tool wear). Various features were extracted so that artificial intelligence can learn effectively from signals. It was trained with 5 features with excellent distinguishing performance, and the SVM algorithm performance was the best among 33 learning models. As a result of the research, the vibration signal at the time of imminent tool replacement was matched with an accuracy of more than 85%. It is expected that the results of this research will solve problems such as tool damage due to accidental punch breakage at the production site, and increase in maintenance costs due to prediction errors in punch exchange cycles due to wear.

A earn profile grinding machine is a mandatory machine tool for manufacture of high precision contoured cam. Experimental evaluation of modal analysis is an effective tool to investigate dynamic behavior of a machine. This paper presents the measurement system and experimental investigation on the modal analysis of a grinding machine. The weak part of the machine is found by the experimental evaluation. The results provide structure modification data for good dynamic behaviors. And safety of the machine was confirmed by the modal analysis of modified machine design. Finally, the cam profile grinding machine was successfully developed.

The objective of this study is to design an optimal DVA(dynamic vibration absorber) which will reduce washing machine vibration for dehydration cycle. A theoretical model for DVA is presented, which describes optimal solution and design variables such as mass ratio, damping ratio and resonance frequency ratio. Comprehensive experiments were carried out concerning the washing machine vibration for dehydration cycle and verified the vibration absorption performance. In conclusion, washing machine vibration decreased over 60 % at the both resonance RPM and max. RPM with optimized DVA.