• 비파괴검사학회지
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• 제16권3호
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• pp.155-161
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• 1996

회전 기계의 저어널 베어링 상태를 음향 방출(AE) 기술을 활용하여 감시하기 위한 진단 시스템을 개발하였다. AE 기술은 베어링 시스템에 있어서 비정상 상태를 탐지하기 위하여 이용된다 모의 저어널 베어링시스템을 이용한 실험과 실제 발전 설비에 대한 적용 시험의 결과로부터 AE 신호 파라메타 중에서 rms voltage가 가장 유효한 것으로 판명되었으며, 이러한 연구 결과를 토대로 하여 진단 시스템의 알고리즘과 판단 기준들이 설정되었다. 베어링 진단 시스템은 AE 센서 및 전치앰프로 구성된 신호 감지부, AE rms voltage를 측정하기 위한 rms-to-DC 변환 회로부로 구성된 신호 처리부, A/D 변환기를 이용하여 rms voltage 신호를 PC에 연결해 주는 인터페이스부, 베어링 상태 보기와 진단 프로그램을 포함하는 그래픽 디스플레이 및 소프트웨어부의 4부분으로 구성된다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.585-590
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• 2004

Many machine failures are not detected well in advance due to the masking of background noise and attenuation of the source signal through the transmission mediums. Advanced signal processing techniques using adaptive filters and higher order statistics have been attempted to extract the source signal from the measured data at the machine surface. In this paper, blind deconvolution using the eigenvector algorithm (EVA) technique is used to recover a damaged bearing signal using only the measured signal at the machine surface. A damaged bearing signal corrupted by noise with varying signal-to-noise (s/n) was used to determine the effectiveness of the technique in detecting an incipient signal and the optimum choice of filter length. The results show that the technique is effective in detecting the source signal with an s/n ratio as low as 0.21, but requires a relatively large filter length.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2004년도 춘계학술발표회
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• pp.11-19
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• 2004

When pile foundation constructed by driving method, it is desirable to perform monitoring and estimation of pile drivability and bearing capacity using some suitable tools. Dynamic Pile Monitoring yields information regarding the hammer, driving system, and pile and soil behaviour that can be used to confirm the assumptions of wave equation analysis. Dynamic Pile Monitoring is performed with the Pile Driving Analyser. The Pile Driving Analyser (PDA) uses wave propagation theory to compute numerous variables that fully describe the condition of the hammer-pile-soil system in real time, following each hammer impact. This approach allows immediate field verification of hammer performance, driving efficiency, and an estimate of pile capacity. The PDA has been used widely as a most effective control method of pile installations. A set of PDA test was performed at the site of Donghea-1 Gas Platform Jacket which is located east of Ulsan. The drilling core sediments of location of jacket subsoil are composed of mud and sand, silt. In this case study, the results of PDA test which was applied to measurement and estimation of large diameter open ended steel pipe pile driven by underwater hydraulic hammer, MHU-800S, at the marine sediments were summarized.

• 조명전기설비학회논문지
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• 제19권6호
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• pp.36-45
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• 2005

이 논문은 다른 종류의 유도전동기 구름베어링 손상을 유도전동기 고정자 전류신호해석을 통하여 검출하고 실시간으로 손상을 진단하는 알고리즘을 개발하였다. 유도전동기 구름베어링의 손상을 검출하기 위하여 정상적인 베어링을 갖는 유도전동기, 측정열에 불량을 가지고 있는 전동기와 베어링 외륜에 구멍을 가지고 있는 2가지 종류의 비정상 베어링을 갖는 유도전동기 3set를 실험시스템을 구축하였다. 또한 유도전동기의 구름베어링시스템의 비정상적인 상태에서 고정자전류을 검출하기 위하여 TMS320F2407 DSP 칩을 이용하여 데이터 획득보드를 개발하였다. 이 고정자전류신호를 해석을 통하여 베어링 손상을 검출하기 위한 방법으로 FFT, 웨이브렛 분석 및 내적에 의한 평균 신호패던에 의한 분석결과를 제시하였다. 특히 내적에 의한 신호분석 온 통하여 베어링 손상 여부를 실시간으로 진단할 수 있는 새로운 알고리즘과 분석방법을 제시하였다.

• 대한기계학회논문집A
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• 제32권11호
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• pp.905-910
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• 2008

Vibration analysis is widely used in machinery diagnosis and the wavelet transform has also been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether acoustic signal can be used effectively along vibration signal to detect the various local fault, in local fault of gearboxes using the wavelet transform. Moreover, envelop analysis is well known as useful tool for the detection of rolling element bearing fault. In this paper, a acoustic emission (AE) sensor is employed to detect gearbox damage by installing them around bearing housing at driven-end side. Signal processing is conducted by wavelet transform and enveloping to detect her fault all at once gearbox using AE signal.

• 한국안전학회지
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• 제21권1호
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• pp.6-14
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• 2006

The development of automatic production systems have required intelligent diagnostic and monitoring functions to overcome system failure and reduce production loss by the failure. In order to perform accurate operations of the intelligent system, implication about total system failure and fault analysis due to each mechanical component failures are required. Also solutions for repair and maintenance can be suggested from these analysis results. As an essential component of a mechanical system, a bearing system is investigated to define the failure behavior. The bearing failure is caused by lubricant system failure, metallurgical deficiency, mechanical condition(vibration, overloading, misalignment) and environmental effects. This study described slab transfer equipment fault train due to stress variation and metallurgical deficiency from lubricant failure by using FTA.

• 한국음향학회지
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• 제35권3호
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• pp.192-201
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• 2016

풍력발전설비는 접근성의 문제로 주기적인 구조건전성 검사를 수행하기 어렵고, 기상상태를 포함한 주위 환경변화 때문에 예기치 못한 고장발생 가능성이 높아 이에 대한 보완책으로 상태감시시스템(Condition Monitoring System, CMS)을 운영하고 있다. 본 연구에서는 CMS의 이상감시 성능 향상을 위하여 풍력발전기 주요 기계시스템에서 장기간 측정된 진동신호 분포를 통계적으로 분석하고, 운전 조건에 따른 진동 변화 경향을 파악한다. 이를 위하여, 풍력발전기 동력전달 및 전력생성부의 진동, 풍속, 주축회전수 등을 약 2년동안 측정한 데이터를 기반으로 운전 환경 및 조건에 따른 각 신호의 경향분석을 수행하고, 기계시스템 구조에 따른 신호별 상호연관성을 분석하였다. 결과적으로 풍력발전기 기계시스템별 진동은 주축회전수, 발전여부에 영향을 받고, 특정 주축회전수에서는 베이불(Weibull) 분포에 해당하는 진동분포가 나타남을 확인하였다. 이런 결과는 풍력발전기 CMS 시스템에서 기계적 이상발생 여부를 조기에 판단하는 기준을 제시할 수 있다.

• Journal of Power Electronics
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• 제17권4호
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• pp.972-982
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• 2017

The Park's vector of stator current is a popular technique for the detection of induction motor faults. While the detection of the faulty condition using the Park's vector technique is easy, the classification of different types of faults is intricate. This problem is overcome by the Multiple Park's Vector (MPV) approach proposed in this paper. In this technique, the characteristic fault frequency component (CFFC) of stator winding faults, rotor winding faults, unbalanced voltage and bearing faults are extracted from three phase stator currents. Due to constructional asymmetry, under the healthy condition these characteristic fault frequency components are unbalanced. In order to balanced them, a correction factor is added to the characteristic fault frequency components of three phase stator currents. Therefore, the Park's vector pattern under the healthy condition is circular in shape. This pattern is considered as a reference pattern under the healthy condition. According to the fault condition, the amplitude and phase of characteristic faults frequency components changes. Thus, the pattern of the Park's vector changes. By monitoring the variation in multiple Park's vector patterns, the type of fault and its severity level is identified. In the proposed technique, the diagnosis of faults is immune to the effects of unbalanced voltage and multiple faults. This technique is verified on a 7.5 hp three phase wound rotor induction motor (WRIM). The experimental analysis is verified by simulation results.

• Smart Structures and Systems
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• 제30권6호
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• pp.557-569
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• 2022

Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.

• 풍력에너지저널
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• 제14권3호
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• pp.34-42
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• 2023

The reduction of operation and maintenance (O&M) costs is a critical factor in determining the competitiveness of wind energy. Predictive maintenance based on the estimation of remaining useful life (RUL) is a key technology to reduce logistic costs and increase the availability of wind turbines. Although a mechanical component usually has sudden changes during operation, most RUL estimation methods use the trend of a state index over the whole operation period. Therefore, overestimation of RUL causes confusion in O&M plans and reduces the effect of predictive maintenance. In this paper, two RUL estimation methods (load based and data driven) are proposed for the bearings of a wind turbine with the results of trend classification, which differentiates constant and increasing states of the state index. The proposed estimation method is applied to a bearing degradation test, which shows a conservative estimation of RUL.