• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1439-1444
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• 2005

Induction motors are the most commonly used electrical drives because they are rugged, mechanically simple, adaptable to widely different operating conditions, and simple to control. The most common faults in squirrel-cage induction motors are bearing, stator and rotor faults. Surveys conducted by the IEEE and EPRI show that the most common fault in induction motor is bearing failure (${\sim}$40% of failure). Thence, this paper addresses experimental results for diagnosing faults with different rolling element bearing damage via motor current spectral analysis. Rolling element bearings generally consist of two rings, an inner and outer, between which a set of balls or rollers rotate in raceways. We set the experimental test bed to detect the rolling-element bearing misalignment of 3 type induction motors with normal condition bearing system, shaft deflection system by external force and a hole drilled through the outer race of the shaft end bearing of the four pole test motor. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by incipient bearing failure. The failure modes are reviewed and the characteristics of bearing frequency associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and related frequencies are also determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. We utilized the FFT, Wavelet analysis and averaging signal pattern by inner product tool to analyze stator current components. The test results clearly illustrate that the stator signature can be used to identify the presence of a bearing fault.

• 한국기계가공학회지
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• 제11권4호
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• 한국자동차공학회논문집
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• 제11권2호
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• pp.89-95
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• 2003

We consider a problem of application of analytical redundancy to engine synchronization process of spark ignition engines, which is critical to timing for every ECU process including ignition and injection. The engine synchronization process we consider here is performed using the pulse signal obtained by the revolution of crankshaft trigger wheel (CTW) coupled to crank shaft. We propose a discrete-time linear model for the signal, for which we construct FDI (Fault Detection & Isolation) system consisting residual generator and threshold based on linear observer.

• Smart Structures and Systems
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• 제23권4호
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• pp.393-403
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• 2019

Applying more features gives us better accuracy in modeling; however, increasing the inputs causes the curse of dimensions. In this paper, a new structure has been proposed for fault detecting and identifying (FDI) of high-dimensional systems. This structure consist of two structure. The first part includes Auto-Encoders (AE) as Deep Neural Networks (DNNs) to produce feature engineering process and summarize the features. The second part consists of the Local Model Networks (LMNs) with LOcally LInear MOdel Tree (LOLIMOT) algorithm to model outputs (multiple models). The fault detection is based on these multiple models. Hence the residuals generated by comparing the system output and multiple models have been used to alarm the faults. To show the effectiveness of the proposed structure, it is tested on single-shaft industrial gas turbine prototype model. Finally, a brief comparison between the simulated results and several related works is presented and the well performance of the proposed structure has been illustrated.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.891-894
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• 2007

This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2007년도 제28회 춘계학술대회논문집
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• pp.245-249
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• 2007

본 연구에서는 모델 기반(Model-Based) 성능진단에 신경회로망을 적용하였고, SIMULINK를 이용하여 PW206C 터보축 엔진의 모델링을 수행하였다. 비행 고도, 비행 마하수, 가스발생기 회전수에 따른 다양한 운용영역의 성능데이터를 base로 하여 압축기, 압축기터빈, 동력터빈의 성능 저하에 대한 학습 데이터를 획득하고 역전파(Back Propagation Network)를 이용하여 훈련 하였다. 설계점 및 탈설계 영역에서 압축기, 압축기터빈, 동력터빈의 단일 손상 탐지를 수행한 결과 손상된 구성품을 잘 탐지함을 확인할 수 있었다.

• 한국추진공학회지
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• 제11권3호
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• pp.29-34
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• 2007

본 연구에서는 모델 기반(Model-Based) 성능진단에 신경회로망을 적용하였고, SIMULINK를 이용하여 PW206C 터보축 엔진의 모델링을 수행하였다. 비행 고도, 비행 마하수, 가스발생기 회전수에 따른 다양한 운용영역의 성능데이터를 base로 하여 압축기, 압축기터빈, 동력터빈의 성능 저하에 대한 학습데이터를 획득하고 역전파(Back Propagation Network)를 이용하여 훈련하였다. 설계점 및 탈설계 영역에서 압축기, 압축기터빈, 동력터빈의 단일 손상 탐지를 수행한 결과 손상된 구성품을 비교적 잘 탐지함을 확인할 수 있었다.

• 한국수소및신에너지학회논문집
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• 제26권2호
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• pp.192-198
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• 2015

Fault Tree Analysis to predict the lifetime in the design process of LNG compressor is considered. Fault Trees for P & ID of the compressor are created. Individual components that comprise the compressor are configured with the basic event. The failure rates in the PDS and OREDA are applied. As results, the system failure rate and the reliability over time are obtained. Further, the power transmission and the shaft seal system is confirmed to confidentially importantly contribute to the overall lifetime of the system. These techniques will help to improve the reliability of design of large scale machinery such as a plant.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.505-508
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• 2014

Transmission error (TE) is defined as "the angular difference between the ideal output shaft position and actual position". As TE is one of the major source of the noise and vibration of gears, it is originally studied with relation of the noise and vibration of the gears. However, recently, with the relation of mesh stiffness, TE has been studied for fault detection of spur gear sets. This paper presents a physics-based theory on fault diagnostics of a planetary gear using transmission error. After constructing the lumped parameter model using DAFUL, multi-body dynamics software, we developed a methodology to diagnose the faults of the planetary gear including phase calculation, signal processing. Using developed methodology, we could conclude that TE could be a good signal for fault diagnostics of a planetary gear.

• 한국항공우주학회지
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• 제37권6호
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• pp.556-561
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• 2009

다양한 비행환경에서 장시간 체공하며 원격 조종되는 무인항공기에서 추진시스템을 신뢰성 있게 운영하는 것은 매우 중요하다. 스마트 무인기의 수직 이착륙 및 전진 비행에 사용 되는 터보축엔진의 정확한 손상진단은 신뢰성과 이용률을 향상시킬 수 있을 것이다. 본 연구에서는 엔진 측정 파라미터들의 변화로부터 퍼지이론을 적용하여 손상된 구성품을 식별한 후 훈련된 신경망 알고리즘을 식별된 손상 패턴에 적용 손상된 양을 정확히 진단할 수 있는 방법을 새로이 제안하였다. 제안된 진단방법은 단일손상은 물론 다중손상도 진단할 수 있다.