153 research outputs found

    ๋ฌผ๋ฅ˜์ž๋™ํ™” ์‹œ์Šคํ…œ์˜ ๊ณ ์žฅ์ง„๋‹จ์„ ์œ„ํ•œ ํŠน์ง• ๋ถ„์„ ๋ฐ ๊ตฐ์ง‘ ์ ์‘ํ˜• ๋„คํŠธ์›Œํฌ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๋ถ€, 2022.2. ์œค๋ณ‘๋™.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.๋ณธ ๋…ผ๋ฌธ์€ ๋ฐ˜๋„์ฒด ๊ณต์žฅ์˜ ๋ฌผ๋ฅ˜์ž๋™ํ™” ์‹œ์Šคํ…œ (AMHS)์˜ ๊ณ ์žฅ ์ง„๋‹จ์„ ์œ„ํ•œ ํŠน์ง• ๋ถ„์„ ๋ฐ ๊ตฐ์ง‘ ์ ์‘ํ˜• ๋„คํŠธ์›Œํฌ (FAFAN)๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋ฐ˜๋„์ฒด ๊ณต์žฅ AMHS์˜ ํ•ต์‹ฌ์ธ ์ฒœ์žฅ ๋ฐ˜์†ก ์‹œ์Šคํ…œ (OHT) ๊ตฐ์ง‘์— ๋Œ€ํ•œ ๊ณ ์žฅ ์ง„๋‹จ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์€, ๊ฐ OHT ํ˜ธ๊ธฐ๋ณ„๋กœ ํ† ํฌ ์‹ ํ˜ธ์˜ ํŽธ์ฐจ๊ฐ€ ์กด์žฌํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์–ด๋ ต๋‹ค. ๋˜ํ•œ, ๋งŽ์€ ํ˜ธ๊ธฐ์—์„œ ์ทจ๋“๋˜๋Š” ์‹ ํ˜ธ๋Š” ์ •์ƒ/๊ณ ์žฅ ๋ ˆ์ด๋ธ”์ด ์žˆ๋Š” ๋ฐ์ดํ„ฐ์™€ ๋ ˆ์ด๋ธ”์ด ์—†๋Š” ๋ฐ์ดํ„ฐ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€, ์ „์ฒ˜๋ฆฌ๋œ ๋‹ค์ฑ„๋„ ํ† ํฌ ์‹ ํ˜ธ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณ ์žฅ์„ ์ง„๋‹จํ•จ๊ณผ ๋™์‹œ์— OHT ํ˜ธ๊ธฐ ๋„๋ฉ”์ธ์— ๋Œ€ํ•œ ์ผ๋ฐ˜์ ์ธ ํŠน์ง•์„ ํ•™์Šตํ•œ๋‹ค. ํŠนํžˆ, ์ „์ฒ˜๋ฆฌ๋œ ์ž…๋ ฅ ์ฑ„๋„์—์„œ ํŠน์ง•์„ ๋…๋ฆฝ์ ์œผ๋กœ ์ถ”์ถœํ•˜๊ณ  ๋„๋ฉ”์ธ ์ผ๋ฐ˜ํ™” ๊ฐ€๋Šฅ์„ฑ๊ณผ ๊ณ ์žฅ ์ง„๋‹จ์˜ ์ •๋ณด๋Ÿ‰์„ ๋ชจ๋ธ ํ•™์Šต ๊ณผ์ •์— ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•ด ์ฑ„๋„ ๊ฐ€์ค‘์น˜๋ฅผ ์ž๋™์œผ๋กœ ํ•™์Šตํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ 1) ๋ ˆ์ด๋ธ”์ด ์žˆ๋Š” ๋ฐ์ดํ„ฐ๋กœ๋งŒ ๊ตฌ์„ฑ๋œ ์†Œ์Šค ๋„๋ฉ”์ธ๊ณผ, ๋ ˆ์ด๋ธ”์ด ์žˆ๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ๋งค์šฐ ์ ์€ ํƒ€๊ฒŸ ๋„๋ฉ”์ธ์œผ๋กœ OHT ๋ฐ์ดํ„ฐ์„ธํŠธ๋ฅผ ๋‚˜๋ˆ„๋Š” ๋‹จ๊ณ„, 2) ์ „๋ฉด ๋ฐ ํ›„๋ฉด ํ† ํฌ ์‹ ํ˜ธ๋ฅผ 3์ฑ„๋„ ์‹ ํ˜ธ๋กœ ์ „์ฒ˜๋ฆฌํ•˜๋Š” ๋‹จ๊ณ„, ๊ทธ๋ฆฌ๊ณ  3) ์ค€์ง€๋„ ๋„๋ฉ”์ธ ์ ์‘์„ ํ™œ์šฉํ•˜์—ฌ OHT ํ˜ธ๊ธฐ ๋„๋ฉ”์ธ ๊ฐ„์˜ ์‹ ํ˜ธ ํŽธ์ฐจ๋ฅผ ์ตœ์†Œํ™”ํ•จ๊ณผ ๋™์‹œ์— ์ •์ƒ, ๋ฐ”ํ€ด ๊ฒฐํ•จ ๋ฐ ๊ธฐ์–ด ๊ฒฐํ•จ ์ƒํƒœ๋กœ ๋ถ„๋ฅ˜ํ•˜๋Š” ํŠน์ง•์„ ์ถ”์ถœํ•˜๋Š” ๋‹จ๊ณ„๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์‹ค์ œ ์‚ฐ์—… ํ˜„์žฅ์—์„œ ์ˆ˜์ง‘๋œ 20๊ฐœ์˜ OHT ํ˜ธ๊ธฐ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด, ๋งŽ์€ OHT ํ˜ธ๊ธฐ ๋„๋ฉ”์ธ ์กฐํ•ฉ ๋ฐ ํƒ€๊ฒŸ ๋„๋ฉ”์ธ ๋ ˆ์ด๋ธ” ๋ฐ์ดํ„ฐ์˜ ๋‹ค์–‘ํ•œ ๋น„์œจ ์กฐํ•ฉ์„ ํ™œ์šฉํ•˜์—ฌ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์˜ ํšจ๊ณผ๋ฅผ ์ž…์ฆํ•œ๋‹ค. ๊ฒ€์ฆ ๊ฒฐ๊ณผ, ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ๋ถˆ์ถฉ๋ถ„ํ•œ ๋ ˆ์ด๋ธ” ๋ฐ์ดํ„ฐ ์กฐ๊ฑด์—์„œ OHT ๊ตฐ์ง‘์˜ ๊ณ ์žฅ ์ง„๋‹จ์— ํšจ๊ณผ์ ์ด๋ฉฐ, ๋‚˜์•„๊ฐ€ ์ง„๋‹จ ๊ฒฐ๊ณผ์˜ ๋ฌผ๋ฆฌ์  ๊ทผ๊ฑฐ๋ฅผ ์ œ๊ณตํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค.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 ๊ตญ๋ฌธ ์ดˆ๋ก 42์„

    Modelling of spreader hoist systems in mobile gantry cranes

    Get PDF

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

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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

    Full text link
    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

    Get PDF
    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
    • โ€ฆ
    corecore