A New Study on Vibration Data Acquisition and Intelligent Fault Diagnostic System for Aero-engine
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- Proceedings of the Korean Society of Propulsion Engineers Conference
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- 2008.03a
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- pp.16-21
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- 2008
Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.
과거 2003년 북미 대 정전 이후 전력기기의 사고 발생 후 얼마나 빨리 사고를 제거하고 피해가 적도록 신속하게 복구하는 개념에서 사고이전에 사고를 미연에 방지하는 예방개념으로 관심이 높아지고 있다. 전력기기를 사고로부터 보호하는 보호기기도 중요하지만 사고이전의 상태를 감시하여 미연에 사고를 방지할 수 있도록 하는 예방진단시스템의 중요성도 높아지고 있다. 이렇듯 관심이 높아짐에 따라 각종 진단알고리즘의 개발이 신속히 이루어지고 있다. 보호기기처럼 어떤 설정된 정정 값 이상의 값이 입력되면 보호동작을 수행하는 단순 동작과는 달리 예방진단 시스템은 입력되는 신호의 패턴을 인식하여 열화/노화 등의 진행상황 및 정비조치에 대한 정보를 만들므로 인공지능적인 요소가 많이 적용되고 있다. 따라서 각종 Fuzzy, Neural Network, Expert 등 각종 판단 알고리즘과 패턴을 인식하는 확률통계, 프랙탈 기하학 등이 적용되고 있다. 모두가 틀리다는 것은 아니지만 보다 정확한 예방진단을 위해 각종 알고리즘이 추가 및 수정이 자주이루어지고 있는 실정이다. 그러나 새로운 알고리즘을 적용하기 위해서 기 개발되어 운영 중이거나 설치된 예방진단시스템을 멈추고 전반적으로 수정을 수행하는 것은 감시진단시스템의 본래 모습을 무시하는 행동이라고 할 수 있다. 본 연구에서는 이런 문제를 해결하기 위하여 온라인 상태에서 장비를 감시하는 예방진단 시스템의 알고리즘 변형 시 시스템의 운영이 문제되지 않도록하는 다이나믹 인터페이스를 개발하였다.
Social movements to improve the performance of buildings through remodeling of aging apartment houses are being captured. To this end, the remodeling construction cost analysis, structural analysis, and political institutional review have been conducted to suggest ways to activate the remodeling. However, although the method of analyzing construction cost for remodeling apartment houses is currently being proposed for research purposes, there are limitations in practical application possibilities. Specifically, In order to be used practically, it is applicable to cases that have already been completed or in progress, but cases that will occur in the future are also used for construction cost analysis, so the sustainability of the analysis method is lacking. For the purpose of this, we would like to suggest an automated estimating method. For the sustainability of construction cost estimates, Deep-Learning was introduced in the estimating procedure. Specifically, a method for automatically finding the relationship between design elements, work types, and cost increase factors that can occur in apartment remodeling was presented. In addition, Monte Carlo Simulation was included in the estimation procedure to compensate for the lack of uncertainty, which is the inherent limitation of the Deep Learning-based estimation. In order to present higher accuracy as cases are accumulated, a method of calculating higher accuracy by comparing the estimate result with the existing accumulated data was also suggested. In order to validate the sustainability of the automated estimates proposed in this study, 13 cases of learning procedures and an additional 2 cases of cumulative procedures were performed. As a result, a new construction cost estimating procedure was automatically presented that reflects the characteristics of the two additional projects. In this study, the method of estimate estimate was used using 15 cases, If the cases are accumulated and reflected, the effect of this study is expected to increase.
We explore automated scoring models of scientific argumentation. We consider how a new analytical approach using a machine learning technique may enhance the understanding of spoken argumentation in the classroom. We sampled 2,605 utterances that occurred during a high school student's science class on molecular structure and classified the utterances into five argumentative elements. Next, we performed Text Preprocessing for the classified utterances. As machine learning techniques, we applied support vector machines, decision tree, random forest, and artificial neural network. For enhancing the identification of rebuttal elements, we used a heuristic feature-engineering method that applies experts' classification of morphemes of scientific argumentation.
In this paper we propose the automated generation algorithm of penetration scenario using association mining technique. Until now known intrusion detections are classified into anomaly detection and misuse detection. The former uses statistical method, features selection, neural network method in order to decide intrusion, the latter uses conditional probability, expert system, state transition analysis, pattern matching for deciding intrusion. In proposed many intrusion detection algorithms unknown penetrations are created and updated by security experts. Our algorithm automatically generates penetration scenarios applying association mining technique to state transition technique. Association mining technique discovers efficient and useful unknown information in existing data. In this paper the algorithm we propose can automatically generate penetration scenarios to have been produced by security experts and is easy to cope with intrusions when it is compared to existing intrusion algorithms. Also It has advantage that maintenance cost is not high.
Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.
Project management in the Construction field inherently has more uncertainty and more risks relative to ones from other area. This is the very reason for why project management is recognized as the important task to construction companies. For getting better performance in the project management, we need a system that keeps the consistencies in a automatic or semi-automatic manner through the project management stages like as project definition stage, project planning stage, project design and implementation stage. But since the early stages such as definition and planning stages has many unstructured features and also are dependent to unique expertise or experience of a specific company, we have difficulty providing systematic support for the task of these stages. This kind of problem becomes harder to solve especially in the plant construction domain that is our target domain. Therefore, in this paper, we propose and also implement a systematic approach to resolve the problem mentioned for the early project management stages in the plant construction domain. The results of our approach can be used not only for the purpose of the early project management stages but also can be used automatically as an input to commercial project management tools for the middle project management stages. Because of doing in this way, the construction project can be consistently managed from the definition to implementation stage in a seamless manner. For achieving this purpose, we adopt knowledge based inference, CBR, and neural network as major methodologies and we also applied our approach to two real world cases, power plant and drainage treatment plant cases from a leading construction company in Korea. Since these two application cases showed us very successful results, we can say our approach was validated successfully to the plant construction area. Finally, we believe our approach will contribute to many project management problems from more broader construction area.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70