• Smart Structures and Systems
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• 제29권6호
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• pp.785-790
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• 2022

Condition monitoring of permanent magnet synchronous motors (PMSMs) and detecting faults such as eccentricity and demagnetization are essential for ensuring system reliability. Motor current signal analysis is the most commonly used precursor for detecting faults in the PMSM drive system. However, the current signature responds sensitively to the load and temperature of the motor, thereby making it difficult to monitor faults in real- applications. Therefore, in this study, a condition monitoring methodology that detects motor faults, including their classification with standstill conditions, is proposed. The objective is to detect and classify faults of PMSMs by using programmable inverter without additional sensors and systems for detection. Both DC and AC were applied through the d-axis of a three-phase motor, and the change in incremental inductance was investigated to detect and classify faults. Simulation with finite element analysis and experiments were performed on PMSMs in healthy conditions as well as with eccentricity and demagnetization faults. Based on the results obtained from experiments, the proposed method was confirmed to detect and classify types of faults, including their severity.

• Journal of Mechanical Science and Technology
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• 제20권12호
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• pp.2013-2024
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• 2006

This paper proposes a condition classification system using wavelet transform, feature evaluation and artificial neural networks to detect faulty products on the production line of reciprocating compressors for refrigerators. The stationary features of vibration signals are extracted from statistical cumulants of the discrete wavelet coefficients and root mean square values of band-pass frequencies. The neural networks are trained by the sample data, including healthy or faulty compressors. Based on training, the proposed system can be used on the automatic mass production line to classify product quality instead of people inspection. The validity of this system is demonstrated by the on-site test at LG Electronics, Inc. for reciprocating compressors. According to different products, this system after some modification may be useful to increase productivity in different types of production lines.

• International Journal of Precision Engineering and Manufacturing
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• 제8권3호
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• pp.54-59
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• 2007

It is widely recognized that increasing the accuracy and diversity of rotating machinery necessitates an appropriate diagnostic technique and maintenance system. Until now, operators have monitored machinery using their senses or by analyzing simple changes to root mean square output values. We developed an expert diagnostic system that uses fuzzy inference to expertly assess the condition of a machine and allow operators to make accurate judgments. This paper describes the hardware and software of the expert diagnostic system. An assessment of the diagnostic performance for five fault phenomena typically found in pumps is also described.

• 한국전기전자재료학회논문지
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• 제12권1호
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• pp.27-34
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• 1999

We investigated the effect of mechanical back side damage in viewpoint of electrical and surface morphological characteristics in Czochralski silicon wafer. The intensity of mechanical damage was evaluated by minority carrier recombination lifetime by laser excitation/microwave reflection photoconductance decay technique, atomic force microscope, optical microscope, wet oxidation/preferential etching methods. The data indicate that the higher the mechanical damage degree, the lower the minority carrier lifetime, and surface roughness, damage depth and density of oxidation induced stacking fault increased proportionally.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.722-732
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• 2018

Dual type Independent multi-phase BLDC Motor (DI-BLDCM) is designed to be robust to faulty conditions of motor and drive system. Despite the efforts of the motor design, open-switch faults of DI-BLDCM drive system cause the torque ripple of the motor. This torque ripple makes unwanted sound noise and mechanical vibration of associated systems. This paper proposes four methods for compensating the torque ripple and compares the characteristics of each proposed method. All proposed methods are able to reduce the torque ripple to similar level of the healthy condition, although the motor operates in open-switch fault conditions. However, these methods have different characteristics in various fault conditions. Therefore, from the results of the comparison, the suitable method is selected for the various fault conditions. The feasibility of the proposed methods is proved by the several experimental results.

• 터널과지하공간
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• 제29권3호
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• pp.197-213
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• 2019

본 연구에서는 TOUGH-FLAC 연동해석기법을 이용하여 Mont Terri 지하연구시설에서 수행된 단층 내 물 주입시험을 수치적으로 모델링하고, 단층의 재활성과 수리역학적 거동 특성을 살펴보았다. TOUGH2 해석에서는 단층을 Darcy의 법칙과 삼승법칙(Cubic law)을 따르는 연속체 요소로 모델링하였으며, FLAC3D 해석에서는 미끄러짐과 개폐가 허용되는 불연속 인터페이스 요소를 통해 모사하였다. 현장에서 획득한 단층의 균열개방압력(fracture opening pressure), 주입율, 모니터링 압력, 변위 곡선 등을 바탕으로, 단층의 탄성적 변형과 파괴에 의한 수직팽창 특성을 반영할 수 있는 수리간극모델과 수리역학 커플링 관계를 해석모델에 반영하였다. 한편, 현지응력 조건, 단층의 강도 및 변형 특성에 따른 파라미터 해석을 실시하여 각 입력변수가 해석 결과에 미치는 영향을 분석하였으며, 이를 통해 현장시험 결과를 가장 잘 재현할 수 있는 파라미터 조합을 선정하였다. 해석 결과, 균열개방압력에서 단층의 주입율과 모니터링 압력이 크게 증가하는 현상을 합리적으로 재현할 수 있었다. 하지만, 동일한 입력 변수 조건에서 단층의 전단변위와 파괴영역의 범위는 현장시험 결과에 비해 과대평가되는 결과를 보였다. 이는 해석모델에서는 고압의 주입조건에서 단층의 지속적인 전단파괴가 유도되는 반면, 현장에서는 수리간극의 변화가 전단 미끄러짐보다는 인장력에 의한 단층면의 개방(tensile opening)에 크게 의존하는 것으로 추정되기 때문이다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.333-338
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• 2001

Fault tree is a widely used methodology for analyzing product reliability. The fault trees are usually constructed using the experiences of expert reliability engineers in top-down approaches and have different structures according to each expert's subjectivity. In this work it is tried to find a general method for the fault tree construction based on the function tree that is the result of product function deployment. Based on the function tree, the method has the advantage of resulting an objective fault tree since the faults are defined as the opposite concept of functions. The fault tree construction of this work consists of the following steps: 1) definition of product primary function with the viewpoints of product operation and configuration, 2) construction of functional relation chart using a grouping algorithm, 3) abstraction of functional block diagram according to operation sequences and configuration of a product, 4) construction of function tree for each viewpoint, and 5) construction of fault tree by matching the function tree and simplification of the result.

• 한국산업융합학회 논문집
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• 제7권2호
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• pp.199-205
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• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.

• 대한기계학회논문집A
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• 제34권11호
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• pp.1681-1689
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• 2010

Support Vector Machine(SVM)을 이용한 회전기계 진단 연구가 많이 수행되어 왔으나 결함 분류성능은 입력 특징과 더불어 다중 분류 방법, 이진분류기, 커널함수 등에 따라 다르다. SVM 을 이용한 대부분의 기존 연구들은 한번 입력 특징들을 선정하면 결함 분류시 동일한 특징데이터를 이용한다. 본 논문에서는 회전기계의 다양한 결함조건에서 측정한 진동신호로부터 추출한 통계적 특징들을 이용하여 각각의 결함을 분류하기 위한 최적 특징들을 선정한 후, 해당 결함상태를 분류하기 위한 SVM 학습과 분류에 각각 이용하였다. 실험자료를 이용한 검증 결과, 제안한 단계 분류 방법이 상대적으로 적은 학습시간으로 단일 다중 분류 방법과 유사한 분류 성능을 얻을 수 있었다.

• 대한기계학회논문집B
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• 제34권4호
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• pp.333-339
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• 2010

고장감지 및 진단(FDD) 시스템의 구축의 기초 연구로 정상상태 진단기에 대한 연구를 수행하였다. 정상상태에 대한 진단은 시스템 전체를 관찰하거나 몇몇 필요한 시스템 파라미터를 모니터링 함으로써 가능하다. 최적화된 정상상태 진단기를 이용하면 FDD 시스템에서 필수적인 정상운전 시의 기준모델(no fault reference model)을 자가학습을 통하여 적용할 수 있다. 본 연구에서는 가정용 열펌프가 냉방조건으로 작동할 경우에 대해 이동창을 기반으로 7개의 측정값들에 대한 표준편차를 분석함으로써 정상상태 판정을 내리도록 하였다. 정상상태 진단기의 작동의 여부는 실내부하를 조절함으로써 확인하였다. 본 연구를 통하여 열펌프 등의 증기압축 사이클 시스템에 대하여 이동창을 기반으로 한 정상상태 진단기 개발 방법을 제시하였다.