물류자동화 시스템의 고장진단을 위한 특징 분석 및 군집 적응형 네트워크 연구

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022.2. 윤병동.This paper proposes a Feature-analytic, Fleet-adaptive Network (FAFAN) for fault diagnosis of automated material handling systems (AMHSs) in semiconductor fabs. Constructing a fault-diagnosis model for a fleet of Overhead Hoist Transports (OHTs), which are the central part of AMHSs in semiconductor fabs, is challenging since the torque signals from different OHT units diverge from each other; further, the signals from many units consist of both labeled data and unlabeled data. To effectively deal with this situation, the proposed method learns fault-discriminative and OHT unit-domain-invariant features by selectively using pre-processed, multi-channel torque signals. Next, the approach independently extracts features from each channel and automatically learns the channel weights to leverage them, considering domain generalizability and the presence of fault signatures. The proposed method consists of three main steps; 1) dividing the OHT dataset into a fully labeled source domain and a sparsely labeled target unit domain, 2) pre-processing front and rear torque signals into three-channel signals, and 3) extracting features to classify signals into normal, wheel fault, and gear fault states, while minimizing domain discrepancy through the use of semi-supervised domain adaptation. We demonstrate the effectiveness of the proposed method using data from 20 OHT units gathered from an actual industrial line, in numerous combinations of OHT unit domains, and different portions of target-domain-labeled data. The results of the validation verify that the proposed method is effective for fault diagnosis of a group of OHTs under insufficient label conditions and, further, that it provides physical evidence of the diagnosing conditions.본 논문은 반도체 공장의 물류자동화 시스템 (AMHS)의 고장 진단을 위한 특징 분석 및 군집 적응형 네트워크 (FAFAN)를 제안한다. 반도체 공장 AMHS의 핵심인 천장 반송 시스템 (OHT) 군집에 대한 고장 진단 모델을 구축하는 것은, 각 OHT 호기별로 토크 신호의 편차가 존재하기 때문에 어렵다. 또한, 많은 호기에서 취득되는 신호는 정상/고장 레이블이 있는 데이터와 레이블이 없는 데이터로 구성되어 있다. 이러한 상황에서 제안된 방법은, 전처리된 다채널 토크 신호를 활용하여 고장을 진단함과 동시에 OHT 호기 도메인에 대한 일반적인 특징을 학습한다. 특히, 전처리된 입력 채널에서 특징을 독립적으로 추출하고 도메인 일반화 가능성과 고장 진단의 정보량을 모델 학습 과정에 활용하기 위해 채널 가중치를 자동으로 학습한다. 제안된 방법은 1) 레이블이 있는 데이터로만 구성된 소스 도메인과, 레이블이 있는 데이터가 매우 적은 타겟 도메인으로 OHT 데이터세트를 나누는 단계, 2) 전면 및 후면 토크 신호를 3채널 신호로 전처리하는 단계, 그리고 3) 준지도 도메인 적응을 활용하여 OHT 호기 도메인 간의 신호 편차를 최소화함과 동시에 정상, 바퀴 결함 및 기어 결함 상태로 분류하는 특징을 추출하는 단계로 구성된다. 실제 산업 현장에서 수집된 20개의 OHT 호기 데이터에 대해, 많은 OHT 호기 도메인 조합 및 타겟 도메인 레이블 데이터의 다양한 비율 조합을 활용하여 제안된 방법의 효과를 입증한다. 검증 결과, 제안된 방법이 불충분한 레이블 데이터 조건에서 OHT 군집의 고장 진단에 효과적이며, 나아가 진단 결과의 물리적 근거를 제공함을 확인하였다.Abstract i List of Tables vi List of Figures vii Nomenclatures viii Chapter 1. Introduction 1 1.1 Research motivation 1 1.2 Research scope 3 1.3 Dissertation Layout 4 Chapter 2. Background 5 2.1 Overhead Hoist Transport (OHT) 5 2.2 Characteristics of the control torque signals of OHTs 6 Chapter 3. Proposed method 9 3.1 Configuration of the proposed FAFAN method 10 3.1.1 Pre-processing module 12 3.1.2 Feature extractor F: Channel-independent CNN 13 3.1.3 Feature extractor F: Channel-weighting block 13 3.1.4 Task module: Condition classifier C & Domain discriminator D 16 3.2 Model training procedures 16 3.2.1 Train F and C to classify the condition 16 3.2.2 Train D using to discriminate the OHT unit domain 17 3.2.3 Train F, C, and D to learn generalized feature representation for the source and target domains 18 Chapter 4. Experimental validation 20 4.1 Dataset description 21 4.2 Description of the comparison methods 23 4.2.1 Source only (S-only) 23 4.2.2 Source Target Labeled (STL) 23 4.2.3 Source Target Labeled CSA loss (STL-CSA) 23 4.2.4 Source Target Labeled CSA loss with Maximum Mean Discrepancy (STL-CSA-MMD) 24 4.3 Experimental settings 25 4.4 Results and discussion 28 4.4.1 Performance analysis 28 4.4.2 Input channel investigation 31 Chapter 5. Conclusion 34 5.1 Summary 34 5.2 Contribution 34 5.3 Future work 36 References 37 국문 초록 42석

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program: 1996

The objectives of the program, which began nationally in 1964 and at JSC in 1965 are to (1) further the professional knowledge qualified engineering and science faculty members, (2) stimulate an exchange of ideas between participants and NASA, (3) and refresh the research and teaching activities of participants' institutions, and (4) contribute to the research objectives of NASA centers. Each faculty fellow spent at least 10 weeks at JSC engaged in a research project in collaboration with a NASA JSC colleague

Qualitative Recognition of Typical Loads in Low-Speed Rotor System

While the load variations within the low speed rotor systems affect the operating conditions and mechanical properties, they may also provide information on machine faults. Therefore, load recognition is of great significance in operational monitoring for detecting early warning signs of failure and diagnosing faults. In this paper, five types of typical loads in a low-speed rotor system are qualitatively analyzed. Moreover, a method is presented based on the vibration signals from a low-speed rotor system using the ensemble empirical mode decomposition (EEMD), energy feature extraction, and backpropagation neural network (BPNN). A low-speed rotor test bench was designed and manufactured for load recognition and an experiment was set up based on certain load characteristics. Loading tests for five representative categories were conducted and various vibration signals were collected simultaneously. The EEMD was shown to eliminate the mode mixing seen in traditional EMD, which resulted in a clear decomposition of the signal. Finally, the characteristics were imported into a BPNN after energy feature extraction, and the different types of load were accurately recognized. Comparing the experimental results to existing data, a total recognition rate of 92.38% was achieved, demonstrating that the proposed method is both reliable and efficient

The Journal of Undergraduate Research: Volume 08

This is the complete issue of the South Dakota State University Journal of Undergraduate Research, Volume 8

Control of multiple tele-operated Robotic Bridge Transporters for remote handling of hazardous material

The objective of this research is to develop control of a multiple telerobot system based on Direct Numerical Processing Technology and propose a practically implementable collision avoidance algorithm for two gantry type robots sharing a common workspace; The tele-operatic set-up consists of two Robotic Bridge Transporters which are X-Y-Z positioning overhead bridge type cranes. These cranes consisting of three sub-assemblies for the X, Y and Z motions respectively are actuated by brushless servo drive motors. The DC motors are controlled by Modicon FA3240 automation controllers from a supervisory control station equipped with computer graphics based human-machine interface. Teleoperation is achieved through a programmable logic controller which acts as a command arbiter and data interface between the automation controllers and the supervisory control station computer; The collision avoidance algorithm proposes a collision free approach for a given path, by means of a minimum delay time technique for the two robots. Two examples, one each for a single segment and a two segment path for the two robots were tried out and found to work satisfactorily. The same can be extended to several segments as may be needed. The implementation methodology has been discussed

Low airspeed systems for the naval SH-60 Seahawk aircraft

Pitot-static systems have long been used to measure helicopter airspeed. The Pitot-static system is inaccurate at low airspeeds (below 40 knots) due to the limited sensitivity of the sensor and interference of rotor down wash. Additionally, the Pitot-static system only measures unidirectional airspeed and unlike its fixed wing counterparts the helicopter is not limited to flight in one direction. With the changing roles of the US Navy Seahawk it is imperative that the pilot and aircrew have all the information necessary to safely complete the mission and prolong the life of the aircraft and dynamic components. With the addition of a dipping sonar to the remanufactured SH-60B aircraft (designated SH- 60R) and the conduct of combat search and rescue mission in the Navy\u27s Seahawks the aircraft will spend more time in a hover and will be flown more aggressively than in the past. This thesis examiness the advantages of incorporating a low airspeed system into the modem helicopter, in particular the SH-60 Seahawk. The author examines the low airspeed sensors and systems currently available and gives a brief description of each system\u27s operation. The author examines the challenges of installing a low airspeed sensor onto the SH-60 Seahawk. The author has determined that either a laser velocimeter or an analytical neural network system would be the best approach for a low airspeed system for the SH-60 Seahawk. The author recommends a combined approach be taken to develop both the laser velocimeter and analytical neural network, and incorporate the best system after further flight testing