딥러닝 기반 회전기계 진단을 위한 진동신호 전처리 및 변환 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 윤병동.Large-scale rotating machinery requires a reliable diagnosis method that accurately predicts health state, since these systems are frequently operated in safety-related and mission-critical systems (e.g., turbines in power plants). Among various methods for rotor-system diagnosis, the data-driven approach has received considerable interest from industry and academia. Specifically, the number of research papers on deep learning based rotor system diagnosis has risen steeply in the past few years. Interest is driven, in part, by the fact that deep learning algorithms are applicable to complex systems without the need for a complete comprehension of the physics of the system. However, powerful performance of these diagnosis methods can only be achieved with the use of optimal preprocessing techniques for each target system. Thus, this dissertation focuses on developing preprocessing and transformation steps for a deep learning based diagnosis system for rotating machinery. This work specifically focuses on fluid-film bearing rotor systems. The dissertation investigates three thrusts of preprocessing and transformation of vibration signals: 1) study of the optimal vibration image size, considering filter size, 2) research into a label-based, mini-batch gradient descent method with filter sensitivity analysis, and 3) investigation of a retraining scheme for minor classes in imbalanced data problems. The first research thrust investigates the size of input images for convolutional neural network (CNN) based diagnosis. As a fluid-film bearing rotor system presents directional dependent health states, vibration images that consider both the temporal and the spatial correlations of omnidirectional regeneration (ODR) signals are suggested. Using the generated images, the results show that the ratio of image size to filter size affects the overall performance. Thus, the optimal range of size ratio for the vibration image is derived in this work by analyzing the performance of various ratios. The second research thrust suggests a label-based, mini-batch gradient descent method. As the conventional random mini-batch method generates biased mini-batches in several cases, which leads to decreased overall performance, the proposed method can reduce the bias between mini-batches. In addition, various label-based, mini-batch combinations were studied in this work and their performance deviation was analyzed by filter sensitivity analysis. The result shows that the quantity of properly sensitive filters clearly improves the overall performance of the network. Finally, the last research thrust proposes a retraining scheme for minority class data in imbalanced data set problems. The proposed two-phase approach uses equally labeled mini-batches, proposed in the second thrust, with oversampling of the minor class samples. Furthermore, in the second phase of training, filters with high sensitivity are frozen and filters with low sensitivity are retrained to represent the minor class samples. The resulting method shows increased performance by improving the recognition of the minority class samples in several imbalanced data set problems.Abstract i Table of Contents iv List of Tables viii List of Figures ix Nomenclature xviii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 6 Chapter 2 Literature Review 7 2.1 Overview of Fluid-film Bearing Rotor Systems 7 2.1.1 Structure of Fluid-film Bearing Rotors 8 2.1.2 Data Acquisition of Vibration Signals from Fluid-film Bearing Rotors 9 2.1.3 Analysis of Vibration Signals for Fluid-film Bearing Rotor Systems 13 2.1.4 Summary and Discussion 14 2.2 Overview of Convolutional Neural Network (CNN) based Rotor System Diagnosis 15 2.2.1 Image Recognition by Convolutional Neural Network (CNN) 15 2.2.2 CNN-based Rotor System Diagnosis Based on Vibration Signals 17 2.2.3 Summary and Discussion 18 2.3 Strategy for Deep Learning Based Diagnosis of Class-Imbalanced Data Sets 19 2.3.1 A Data-Level Strategy for Class Imbalanced Data Set Training 20 2.3.2 An Algorithm-Level Strategy for Class Imbalanced Data Set Training 21 2.3.3 Summary and Discussion 22 Chapter 3 Description of Testbed Data 24 3.1 Configuration of the Testbed 24 3.2 Analysis of Vibration Signals for Four Health States 25 Chapter 4 Determining an Optimal Size of Vibration Images Considering Filter Size 32 4.1 Vibration Image Generation by Omnidrectional Regeneration (ODR) 33 4.1.1 Directional Health States in Fluid-film Bearing Rotor Systems 34 4.1.2 Omnidirectional Regeneration of Vibration Signals 45 4.1.3 Vibration Image Generation 46 4.2 Determining the Optimal Ratio Between Vibration Image Size and Filter Size 51 4.2.1 Vibration Image Size with Respect to Filter Size 52 4.2.2 Gradient of Vibration Image 55 4.2.3 Study of the Optimal Ratio of Vibration Image Size to Filter Size 60 Chapter 5 Label-based, Mini-batch Combinations Study by Filter Sensitivity Analysis 67 5.1 Mini-batch Gradient Descent in Convolutional Neural Network 69 5.1.1 Overview of Convolutional Neural Networks (CNN) 69 5.1.2 Mini-batch Gradient Descent 74 5.2 Label-based, Mini-batch Gradient Descent Study 75 5.2.1 Label-based, Mini-batch Generation 76 5.2.2 Filter Sensitivity Analysis 79 5.2.3 Criteria of Properly Sensitive Filters 83 5.3 Description of Data Set 83 5.4 Results of Label-based, Mini-batch Gradient Descent Methods 85 5.4.1 Performance of Label-based, Mini-batch Methods 85 5.4.2 Sensitivity of Filters for Label-based, Mini-batch Methods 93 5.4.3 Correlation between Performance and Sensitive Filters 100 Chapter 6 Retraining the Minor Class Scheme for Imbalanced Data Sets 105 6.1 Preliminary Study of the Imbalanced Data Set Problem 108 6.1.1 Imbalanced Data Sets 108 6.1.2 Equally Labeled Mini-batch by Oversampling 113 6.2 Retraining Scheme for the Minor Class 116 6.2.1 Equally Labeled Mini-batch Method using Oversampling 118 6.2.2 Retraining Low-sensitive Filters for Minor Class Recognition 118 6.3 Results of the Proposed Minor Class Retraining Scheme 121 6.3.1 Overall Performance of the Proposed Method for Retraining the Minor Class Scheme 124 6.3.2 Performance of Minor Class Prediction Accuracy 132 6.3.3 Filter Sensitivity Analysis for the Minor Class 140 6.3.4 Summary and Discussion 146 Chapter 7 147 7.1 Contributions and Significance 147 7.2 Suggestions for Future Research 149 References 152 국문 초록 170Docto

Jeong, Jun-Ha

학위논문(박사)--아주대학교 일반대학원 :건설교통공학과,2007. 2본 연구는 실시간 교통신호제어시스템의 평가에 있어서 기존의 소프트웨어 기반의 평가 시뮬레이션의 한계를 극복할 수 있는 새로운 방식의 평가시스템 개발에 대한 연구이다. 즉, 평가대상 장치나 시스템을 교통류 시뮬레이션 모형에 물리적으로 연결하여 상호교환 작용으로 시스템의 효율성을 평가할 수 있는 시스템인 HILS 기반 평가시스템의 개발에 관한 연구이다. 본 논문의 주요내용은 평가시스템의 핵심요소기술의 제시, 개발된 평가시스템의 신뢰성 평가, 현장 사례적용 등으로 구성되었다.제 1 절 연구의 배경 및 목적 = 1 제 1 절 기존 교통제어시스템 평가방법 = 9 1. 교통제어시스템 효과평가의 목적 = 9 2. 효과평가 방법의 종류 = 9 제 2 절 국내·외 관련연구의 고찰 = 13 1. HILS 기반 평가시스템의 개요 = 13 제 1 절 시스템 구성 = 39 1. 평가시스템의 분류 및 선정 = 39 2. 개발 평가시스템의 물리적 구성 = 41 3. 중계장치의 구성 및 기능 = 42 1. HILSS용 교통류 시뮬레이션 모형의 요구기능 = 45 1. 모형과 제어기 인터페이스 기법 = 52 가. CORSIM 모형에서의 검지기신호 생성 = 60 3. 실시간 등기신호 검출 및 현시갱신 기법 = 64 제 1 절 평가시스템 신뢰성 평가지표 및 방법 = 77 제 2 절 검지기신호 정확도 검증 = 78 제 1 절 현장긴급제어에 대한 효율성 평가 = 101 1. 평가환경 = 102 2. 평가 시나리오 및 평가방법 = 105 3. 현장긴급제어에 대한 효율성 평가 = 107 1. 평가환경 = 123 2. 평가 시나리오 및 평가방법 = 127MasterSimulation has been an effective tool for evaluating various traffic signal control strategies. The main scope of this study is to develop a new simulation tool, named Korean hardware-in-the-loop simulation (HILS), which can overcome the limitations arising from the application of software-only simulation. In this study, the HILS system is systematically classified in terms of system configuration and usage. Under this condition, real-time detector information is generated automatically and transferred to signal control logic in order to verify the accuracy of detector information. This functionality of the system can extend the application areas not only for evaluating various signal control strategies but also for generating various new signal control tactics. The performance of system is tested in terms of an accuracy of detector information generated and delivered. For this process, the information generated from HILS emulator is directly compared with the information from a device on an equal basis. Evaluation is performed with two indices, equality coefficient and independent sample t-test using a SPSS (ver. 12.0). Test results indicate that the occupancy and non-occupancy detector information is very accurate with a 95% significance level both VISSIM and CORSIM model application. In addition, test results on signal phase duration have no significant difference. Two case evaluations are made to validate the feasibility of the HILS system developed. The first case study is to identify the effectiveness of on-site manual control for oversaturated arterial environment, and the second case study is to quantify the effect of exclusive bus signal control for exclusive bus lane operation in the City of Seoul. From on-site manual control test results, it is found that the manual control may generate negative effects on traffic flow in general. However, the input and output traffic volume into a link can be correctly controlled, then the manual control may provide better performance than a traditional signal control strategy for oversaturated arterial operation. In order to apply this manual control, it is recommended that an automation process should be developed because the manual control has no operational knowledge or guideline. The results also indicate that manual control should be limited to very special situations such as existence of unbalanced input and output volume, existence of spare capacity in downstream links, and a bottleneck location. From the second test results, it is found that the average travel time is reduced by 31% by providing an exclusive bus signal control without a negative effect on side streets

New Way of Detecting Vibration of Mechanical Systems by Explainable Deep Learning

1

설명가능한 딥러닝 기술을 이용한 영상기반 기계 시스템의 진동 진단 방법 고찰

2

Development of an underwater construction robot for port construction

This paper presents an unserwater construction robot for rubble mound leveling. The robot system is composed of an underwater robot, underwater camera ,sonar sensors, LBL and magnetic gyroscope sensor. Virtual reality is developed to visualize the robots figure and the topography over the working environment and the robot is virtually tele-operated by an operator in a operating room. We describe the working process to level the rubble mound using the robot and present the performance of the robot through the experimental results in underwater.ize the robots figure and the topography over the working environment and the robot is virtually tele-operated by an operator in a operating room. We describe the working process to level the rubble mound using the robot and present the performance of the robot through the experimental results in underwater.2

수중 거리 측정을 위한 초음파 센서의 개발

본 연구는 수중 거리 측정을 위한 초음파 센서를 개발한다. 초음파 트랜스듀서는 음파를 발신하고 발신된 음파가 물체에 부딪혀 되돌아오는 반사파를 수신한다. 초음파 드라이버는 반사된 물체까지 거리 측정을 위해 음파의 비행시간을 검출해 음속을 곱한다. 본 연구에서는 비행시간을 검출하기 위해 임계값과 상호 상관 기법을 적용하고 그 결과를 보인다. 반사파가 노이즈에 감염되어 신호의 형태가 왜곡될 때 상호상관 기법은 기준 신호와 수신 신호의 유사성을 이용하여 비행시간을 검출한다. 기준 신호를 수중 환경에 따라 다르게 적용해 반사파와 유사성을 높여 센서의 성능을 향상시킨다. 논문에서는 초음파 센서 드라이버를 설명하고 실험환경에 따른 센서의 성능을 분석한다.곱한다. 본 연구에서는 비행시간을 검출하기 위해 임계값과 상호 상관 기법을 적용하고 그 결과를 보인다. 반사파가 노이즈에 감염되어 신호의 형태가 왜곡될 때 상호상관 기법은 기준 신호와 수신 신호의 유사성을 이용하여 비행시간을 검출한다. 기준 신호를 수중 환경에 따라 다르게 적용해 반사파와 유사성을 높여 센서의 성능을 향상시킨다. 논문에서는 초음파 센서 드라이버를 설명하고 실험환경에 따른 센서의 성능을 분석한다.2