Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain shifts, they are often not directly applicable in real-world situations where only healthy data is available in the target domain. To tackle the challenge of extreme domain shift scenarios where only healthy data is available in the target domain, this paper proposes two novel domain knowledge-informed data synthesis methods utilizing the health data map (HDMap). The two proposed approaches are referred to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent the vibration signal of the planetary gearbox as an image-like matrix, allowing for visualization of fault-related features. CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively. In addition to generating realistic faults, the proposed methods introduce scaling of fault signatures for controlled synthesis of faults with various severity levels. A case study is conducted on a planetary gearbox testbed to evaluate the proposed approaches. The results show that the proposed methods are capable of accurately diagnosing faults, even in cases of extreme domain shift, and can estimate the severity of faults that have not been previously observed in the target domain.Comment: Under review / added arXiv identifie

풍력발전 기어박스의 진동 기반 고장진단 프레임워크

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2013. 8. 윤병동.최근 풍력발전기의 신뢰성 문제가 풍력에너지 산업에서 큰 이슈가 되고 있다. 특히 풍력발전기의 기어박스는 유지보수 비용이 크기 때문에, 풍력발전기의 부품 중에서 경제적 위험도가 가장 크다고 평가되고 있다. 지금까지 풍력발전기의 신뢰성을 보장하기 위한 수많은 연구가 진행되었음에도 불구하고, 아직까지 해당 연구 분야는 여러 가지 어려운 문제점에 직면해 있다. 대표적으로 크게 1) 비정상 (non-stationary) 운행 상태로 인해 발생하는 고장진단 기술의 어려움, 2) 특정 풍력발전 단지 내에 수많은 센서로부터 계측되는 방대한 양의 데이터와 등으로 나눌 수 있다. 따라서 본 연구에서는 일반적인 고장진단 과정을 포괄하는 기어박스의 고장진단 프레임워크를 제안한다. 제안된 프레임워크는 방대한 양의 데이터를 효율적으로 관리하는 동시에 정확한 고장진단 기술의 적용을 가능케 한다. 이를 위해 본 학위논문은 1) 풍력발전 운행 데이터의 분류 시스템 개발, 2) 진동기반 고장진단 기술 개발로 구성되어 있다. 첫 번째 연구에서는, 방대한 양의 풍력발전 데이터를 해당 풍력발전기의 거동 특성 (로터 회전 속도, 발전량)에 의거하여 유의미한 네 가지 (Class I. stationaryClass II. quasi-stationaryClass III. non-stationary with high correlationClass IV. non-stationary with no correlation) 클래스와 무의미한 한 가지 (Class V. idle) 클래스로 분류한다. 이후 각 클래스에 해당하는 데이터의 특성에 기반하여 최적의 고장진단 계획을 설계한다. 데이터 분류기법 개발을 위해 영흥 풍력단지로부터 취득한 풍력발전기의 거동 정보를 이용하였다. 두 번째 연구에서는 정의된 클래스 중 두 가지 클래스 (Class I & II)를 토대로 진동기반 고장진단 기술을 개발한다. 고장진단 기술은 보통 신호의 노이즈를 제거하기 위한 시간 동기 평균화 (Time synchronous averaging)과 유의미한 건전성 데이터를 추출하기 위한 오더분석으로 구성될 수 있다. 하지만 풍력발전기의 유성기어박스의 경우 내부에 포함되어 있는 여러 기어들의 복합적인 작용과 더불어 유성 기어의 축이 계속적으로 변하는 문제가 발생하기 때문에 기존의 고장진단 방법을 적용할 수 없다. 따라서 이 논문에서는 풍력발전기의 유성 기어박스에 대한 고장진단을 위해 새로운 시간 동기 평균화 방법인 자기상관함수 기반 시간동기 평균화 (Autocorrelation-based time synchronous averaging) 기법을 개발하였다. 제안된 진동기반 고장진단 기법을 검증하기 위해서 두 가지 신호(수학적 신호, 테스트베드로부터 취득한 신호)가 사용되었다. 이를 위해 우선 두 개의 모터와 메인 베어링, 플라이휠, 기어박스 그리고 13개의 센서 시스템이 구축되어 있는 2kW 풍력발전기 테스트베드가 설계되었다. 특히 인위적 고장이 인가된 기어가 기어박스에 조립될 수 있도록 설계되어 고장진단 연구에 활용할 수 있도록 하였다. 그리고 해당 테스트베드의 거동을 수학적 신호(analytical signal)로 표현하여 고장진단 기법을 사전 검증하였다. 정상 (healthy) 기어박스와 고장(faulty) 기어박스로부터 취득한 신호를 분석하기 위해 자기상관함수 기반 시간동기 평균기법과 오더 분석법을 사용한 결과 제안된 고장진단 기법은 정상 (healthy) 신호와 고장(faulty) 신호를 잘 구별할 수 있었다.Reliability of wind turbines (WT) is a challenging issue in wind energy industry. In particular, a gearbox in a WT has the highest risk because of its high maintenance cost. Despite many prior attempts to develop diagnostics techniques for WTs, one has faced many grand challenges including 1) inaccuracy in fault diagnostics due to random and non-stationary signals and 2) inefficiency in fault diagnostics with big sensory data (e.g. vibration) from many sensors in a WT. This study thus aims at developing a generic guideline and framework for gearbox fault diagnostics. This framework enables accurate diagnostic analysis while working with a massive volume of sensory data from many sensors in an efficient manner. This paper proposes two key ideas in the following research areas as: 1) classification of operational data, and 2) vibration-based fault diagnostics method. First, this study has classified the operation conditions into four non-trivial (Class I. stationaryClass IV. non-stationary with no correlation) conditions and one trivial (Class V. idle) condition in terms of the operation data (rotor speed, and power) of the WTs. Data classification has been conducted with real operational data acquired from Young Heung wind farms. Next, this study has also designed diagnostics methods for the first non-trivial class (Class I) based on the characteristics of the data classes. A core technique for the fault diagnostics is an order analysis method using Time Synchronous Averaging (TSA), where TSA is generally used for signal de-noising and the order analysis for the extraction of health data for a gearbox. It is, however, a daunting task to execute the fault diagnostics using the conventional TSA for a planetary gearbox because of multiple mesh contacts and rotation of the axes of planet gears. This paper proposes a new TSA idea, referred to as Autocorrelation-based TSA (ATSA) for the order analysis, particularly for a planetary gearbox. For the demonstration of the proposed diagnostics framework, two signals were employed: analytical signals and signals from a WT testbed. A 2kW WT testbed was designed with two DC motors, main bearing, flywheel and gearboxes with 13 sensors. A faulty gear was machined with different crack lengths at the root of the gear mesh and assembled into the gearbox. The order analysis based on ATSA processed the signals acquired from the healthy and faulty gearbox. It was concluded that the proposed diagnostics method can distinguish the faulty condition of the gearbox from the healthy one.Abstract i List of Tables vi List of Figures vii Nomenclatures xi Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Scope of research 3 1.3 Structure of the Thesis 4 Chapter 2. Review of Condition Monitoring 5 2.1 SCADA-based Condition Monitoring 5 2.2 Vibration-based Condition Monitoring System(CMS) 7 2.2.1 Spectral Analysis 7 2.2.2 Time-frequency analysis 8 Chapter 3. Classification of Operation Data 11 3.1 Introduction 11 3.2 Classification Method 12 3.3 Criterion for Quantitative Classification 13 3.4 Diagnostics Plans for the Classes 19 3.5 Results and Discussion 22 Chapter 4. Autocorrelation-Based Time Synchronous Averaging 24 4.1 Basic Concept of TSA 24 4.2 Overview of Planetary Gearbox 28 4.3 Conventional TSA for Planetary Gearbox Diagnostics 33 4.4 Autocorrelation-based TSA (ATSA) 38 4.5 Advantages of ATSA 44 Chapter 5. Health Data for WT Gearbox Diagnostics 46 5.1 Review of Health Data for Gearbox Diagnostics 46 5.1.1 GEN 47 5.1.2 RAW 49 5.1.3 TSA 49 5.1.4 RES 50 5.1.5 DIF 52 5.1.6 BPM 53 5.2 Procedures for Calculating Health Data of WT Gearbox 54 Chapter 6. Validation Study for ATSA 56 6.1 Design of Signal 56 6.1.1 Design of the Analytical Signal 56 6.1.2 Design of Testbed 58 6.1.3 Design of Experiment (DOE) 60 6.2 Results and Discussion 61 6.2.1 Analytical Signal 61 6.2.2 Testbed Signal 62 Chapter 7. Conclusion 67 7.1 Conclusion 67 7.2 Future Research 68 Bibliography 70 국문 초록 78 감사의 글 81Maste

Bis[trans-dichloridobis(propane-1,3-diamine-κ2 N,N′)chromium(III)] tetra­chloridozincate determined using synchrotron radiation

In the title compound, [CrCl2(C3H10N2)2]2[ZnCl4], the CrIII atom is coordinated by four N atoms of propane-1,3-diamine (tn) and two Cl atoms in a trans arrangement, displaying a distorted octa­hedral geometry with crystallographic inversion symmetry; the Zn atom in the [ZnCl4]2− anion lies on a -4 axis. The orientations of the two six-membered chelate rings in the complex cation are in an anti chair–chair conformation with respect to each other. The Cr—N bond lengths are 2.087 (6) and 2.097 (6) Å. The Cr—Cl and Zn—Cl bond lengths are 2.3151 (16) and 2.3255 (13) Å, respectively. Weak inter­molecular hydrogen bonds involving the tn NH2 groups as donors and chloride ligands of the anion and cation as acceptors are observed

The Correlation Between Electrodiagnostic Results and Ultrasonographic Findings in the Severity of Carpal Tunnel Syndrome in Females

Objective: To determine which ultrasonographic measurement can be used as an indicator reflecting the severity of carpal tunnel syndrome (CTS), by comparing electrodiagnostic results with ultrasonographic measurements in females. Many previous studies have tried to reveal that the ultrasonography (US) can possibility be used for diagnosis and severity of CTS. However, the criteria are different by gender. Thus far, there have been many efforts towards providing patients with a CTS diagnosis and severity prediction using US, but studies’ results are still unclear due to lack of data on gender differences. Methods: We collected data from 54 female patients. We classified the severity of CTS according to electrodiagnostic results. Ultrasonographic measurements included proximal and distal cross-sectional areas of the median nerve and carpal tunnel. Results: The severity by electrodiagnostic results statistically correlated to the proximal cross-sectional area (CSA) of the median nerve and carpal tunnel. However, there was no relationship between the proximal and distal nerve/tunnel indexes and the severity by electrodiagnostic results. Conclusion: In female patients with CTS, the proximal CSAs of the median nerve and carpal tunnel increase. They correlate with the severity by electrodiagnostic findings. The CSA of the proximal median nerve could be particularly used as a predictor of the severity of CTS in female patients. However, the nerve/tunnel index is constant, irrespective of the severity of CTS

Molecular Weight Dependent Glucose Lowering Effect of Low Molecular Weight Chitosan Oligosaccharide (GO2KA1) on Postprandial Blood Glucose Level in SD Rats Model

Abstract This research investigated the effect of enzymatically digested low molecular weight (MW) chitosan oligosaccharide on type 2 diabetes prevention. Three different chitosan oligosaccharide samples with varying MW were evaluated in vitro for inhibition of rat small intestinal α-glucosidase and porcine pancreatic α-amylase (GO2KA1; \u3c1000 Da, GO2KA2; 1000–10,000 Da, GO2KA3; MW \u3e 10,000 Da). The in vitro results showed that all tested samples had similar rat α-glucosidase inhibitory and porcine α-amylase inhibitory activity. Based on these observations, we decided to further investigate the effect of all three samples at a dose of 0.1 g/kg, on reducing postprandial blood glucose levels in Sprague-Dawley (SD) rat model after sucrose loading test. In the animal trial, all tested samples had postprandial blood glucose reduction effect, when compared to control, however GO2KA1 supplementation had the strongest effect. The glucose peak (Cmax) for GO2KA1 and control was 152 mg/dL and 193 mg/dL, respectively. The area under the blood glucose-time curve (AUC) for GO2KA1 and control was 262 h mg/dL and 305 h mg/dL, respectively. Furthermore, the time of peak plasma concentration of blood glucose (Tmax) for GO2KA1 was significantly delayed (0.9 h) compared to control (0.5 h). These results suggest that GO2KA1 could have a beneficial effect for blood glucose management relevant to diabetes prevention in normal and pre-diabetic individuals. The suggested mechanism of action is via inhibition of the carbohydrate hydrolysis enzyme α-glucosidase and since GO2KA1 (MW \u3c 1000 Da) had higher in vivo effect, we hypothesize that it is more readily absorbed and might exert further biological effect once it is absorbed in the blood stream, relevant to blood glucose management

Impacts of Heavy Rain and Typhoon on Allergic Disease

AbstractObjectivesAllergic disease may be increased by climate change. Recent reports have shown that typhoon and heavy rain increase allergic disease locally by concentration of airborne allergens of pollen, ozone, and fungus, which are causes of allergic disease. The objective of this study was to determine whether typhoon and heavy rain increase allergic disease in Korea.MethodsThis study included allergic disease patients of the area declared as a special disaster zone due to storms and heavy rains from 2003 to 2009. The study used information from the Korea Meteorological Administration, and from the National Health Insurance Service for allergic diseases (asthma, allergic rhinitis, and atopic dermatitis).ResultsDuring a storm period, the numbers of allergy rhinitis and atopic dermatitis outpatients increased [rate ratio (RR) = 1.191; range, 1.150–1.232] on the sixth lag day. However, the number of asthma outpatients decreased (RR = 0.900; range, 0.862–0.937) on the sixth lag day after a disaster period. During a storm period, the numbers of allergic rhinitis outpatients (RR = 1.075; range, 1.018–1.132) and atopy outpatients increased (RR = 1.134; range, 1.113–1.155) on the seventh lag day. However, the number of asthma outpatients decreased to RR value of 0.968 (range, 0.902–1.035) on the fifth lag day.ConclusionThis study suggests that typhoon and heavy rain increase allergic disease apart from asthma. More study is needed to explain the decrease in asthma

Comparison of Operational Definition of Type 2 Diabetes Mellitus Based on Data from Korean National Health Insurance Service and Korea National Health and Nutrition Examination Survey

Background We evaluated the validity and reliability of the operational definition of type 2 diabetes mellitus (T2DM) based on the Korean National Health Insurance Service (NHIS) database. Methods Adult subjects (≥40 years old) included in the Korea National Health and Nutrition Examination Survey (KNHANES) from 2008 to 2017 were merged with those from the NHIS health check-up database, producing a cross-sectional dataset. We evaluated the sensitivity, specificity, accuracy, and agreement of the NHIS criteria for defining T2DM by comparing them with the KNHANES criteria as a standard reference. Results In the study population (n=13,006), two algorithms were devised to determine from the NHIS dataset whether the diagnostic claim codes for T2DM were accompanied by prescription codes for anti-diabetic drugs (algorithm 1) or not (algorithm 2). Using these algorithms, the prevalence of T2DM was 14.9% (n=1,942; algorithm 1) and 20.8% (n=2,707; algorithm 2). Good reliability in defining T2DM was observed for both algorithms (Kappa index, 0.73 [algorithm 1], 0.63 [algorithm 2]). However, the accuracy (0.93 vs. 0.89) and specificity (0.96 vs. 0.90) tended to be higher for algorithm 1 than for algorithm 2. The validity (accuracy, ranging from 0.91 to 0.95) and reliability (Kappa index, ranging from 0.68 to 0.78) of defining T2DM by NHIS criteria were independent of age, sex, socioeconomic status, and accompanied hypertension or dyslipidemia. Conclusion The operational definition of T2DM based on population-based NHIS claims data, including diagnostic codes and prescription codes, could be a valid tool to identify individuals with T2DM in the Korean population

The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea

Background Long-term care facilities (LCFs) in South Korea have limited knowledge of and capability to care for patients with delirium. They also often lack an electronic medical record system. These barriers hinder systematic approaches to delirium monitoring and intervention. Therefore, this study aims to develop a web-based app for delirium prevention in LCFs and analyse its feasibility and usability. Methods The app was developed based on the validity of the AI prediction model algorithm. A total of 173 participants were selected from LCFs to participate in a study to determine the predictive risk factors for delerium. The app was developed in five phases: (1) the identification of risk factors and preventive intervention strategies from a review of evidence-based literature, (2) the iterative design of the app and components of delirium prevention, (3) the development of a delirium prediction algorithm and cloud platform, (4) a pilot test and validation conducted with 33 patients living in a LCF, and (5) an evaluation of the usability and feasibility of the app, completed by nurses (Main users). Results A web-based app was developed to predict high risk of delirium and apply preventive interventions accordingly. Moreover, its validity, usability, and feasibility were confirmed after app development. By employing machine learning, the app can predict the degree of delirium risk and issue a warning alarm. Therefore, it can be used to support clinical decision-making, help initiate the assessment of delirium, and assist in applying preventive interventions. Conclusions This web-based app is evidence-based and can be easily mobilised to support care for patients with delirium in LCFs. This app can improve the recognition of delirium and predict the degree of delirium risk, thereby helping develop initiatives for delirium prevention and providing interventions. Moreover, this app can be extended to predict various risk factors of LCF and apply preventive interventions. Its use can ultimately improve patient safety and quality of care. © 2022, The Author(s).1

Transient Right Ventricular Dysfunction After Pericardiectomy in Patients With Constrictive Pericarditis

Pericardiectomy is the standard treatment in patients with chronic constrictive pericarditis who have persistent symptoms. However, myocardial atrophy with prolonged pericardial constriction and abrupt increase in venous return can lead to heart failure with volume overload after pericardial decompression, especially in the right ventricle (RV). We experienced a 44 year old male patient who developed transient RV failure after pericardiectomy for constrictive pericarditis. Echocardiography revealed a markedly dilated RV with decreased peak systolic velocity of the tricuspid annulus, suggesting severe RV dysfunction. After treatment with inotropics and diuretics, a follow-up echocardiography revealed an improved systolic function with decreased RV chamber size. This case demonstrates the importance of volume overload and RV dysfunction in patients with constrictive pericarditis undergoing pericardiectomy

Fault Log Recovery Using an Incomplete-data-trained FDA Classifier for Failure Diagnosis of Engineered Systems

In the 2015 PHM Data Challenge Competition, the goal of the competition problem was to diagnose failure of industrial plant systems using incomplete data. The available data consisted of sensor measurements, control reference signals, and fault logs. A detailed description of the plant system of interest was not revealed, and partial fault logs were eliminated from the dataset. This paper presents a fault log recovery method using a machine-learning-based fault classification approach for failure diagnosis. For optimal performance, it was critical to be able to utilize a set of incomplete data and to select relevant features. First, physical interpretation of the given data was performed to select proper features for a fault classifier. Second, Fisher discriminant analysis (FDA) was employed to minimize the effect of outliers in the incomplete data sets. Finally, the type of the missing fault logs and the duration of the corresponding faults were recovered. The proposed approach, based on the use of an incomplete-data-trained FDA classifier, led to the second-highest score in the 2015 PHM Data Challenge Competition