복잡한 공학 시스템에 대한 오경보를 고려한 리질리언스 해석 및 설계 방법론 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 윤병동.it estimates a healthy engineered to be faulty, resulting unnecessary system shutdown, inspection, and – in the case of incorrect inspection – unnecessary system repair or replacement. Although false alarms make a system unavailable with capital loss, it has not been considered in resilience engineering. To cope with false alarm problems, this research is elaborated to advance the resilience engineering considering false alarms. Specifically, this consists of three research thrusts: 1) resilience analysis considering false alarms, 2) resilience-driven system design considering false alarms (RDSD-FA), and 3) resilience-driven system design considering time-dependent false alarms (RDSD-TFA). In the first research thrust, a resilience measure is newly formulated considering false alarms. This enables the evaluation of resilience decrease due to false alarms, resulting in accurate analysis of system resilience. Based upon the new resilience measure, RDSD-FA is proposed in the second research thrust. This aims at designing a resilient system to satisfy a target resilience level while minimizing life-cycle cost. This is composed of three hierarchical tasks: resilience allocation problem, reliability-based design optimization (RBDO), and PHM design. The third research thrust presents RDSD-TFA that considers time-dependent variability of an engineered system. This makes one to estimate life-cycle cost in an accurate and rigorous manner, and to design an engineered system more precisely while minimizing its life-cycle cost. The framework of RDSD-TFA consists of four tasks: system analysis, PHM analysis, life-cycle simulation, and design optimization. Through theoretical analysis and case studies, the significance of false alarms in engineering resilience and the effectiveness of the proposed ideas are demonstrated.공학 시스템은 생애주기에 걸쳐 다양한 불확실성에 노출되며, 이로 인해 목표 성능을 충족시키지 못할 경우 사회적, 경계적, 인적 소실을 야기하게 된다. 이에 대한 해결 방안 중 하나로 리질리언스 주도 설계 기술 (resilience-driven system design이하 RDSD)이 개발되었다. RDSD는 건전성 예측 및 관리 기술 (prognostics & health management이하 PHM)을 설계에 도입함으로써 비용 효율적인 고장 예방을 가능케 하였다. 하지만, RDSD는 PHM의 고장 오경보 현상을 고려하지 않는 한계점을 갖는다. 고장 오경보는 건전한 시스템을 고장이라 추정하는 현상으로, 불필요한 시스템 정지 및 검사 비용을 야기하여, PHM과 RDSD의 기술적 효용성을 떨어트리게 된다. 따라서, RDSD의 기술적 약진과 실적용을 도모하기 위해서는 고장 오경보 현상을 해결해야 한다. 본 논문에서는 고장 오경보의 고려를 통해 리질리언스 해석 및 설계 방법론을 개선하고자 하며, 이를 위해 세 가지 연구 주제를 제안한다. 첫 번째 주제는 오경보를 고려한 리질리언스 분석으로, 공학 시스템의 리질리언스 시나리오 분석에 기반해 리질리언스 지수를 새롭게 정식화 한다. 이 지수는 고장 오경보로 인한 리질리언스의 저하를 분석함으로써, 정확한 리질리언스 추정을 가능케 한다. 두 번째 주제는 고장 오경보를 고려한 리질리언스 주도 설계 방법론이다. 이는 3단계의 계층적 요소로 구성된다. 먼저 목표 리질리언스 지수를 만족하면서 생애주기비용을 최소화하기 위해, 목표 신뢰도와 목표 오경보 및 유실경보율을 최적화한다. 이후 신뢰성 기반 최적 설계 (reliability-based design optimization)를 통해 목표 신뢰도를 확보하고, PHM 설계를 통해 할당된 목표 오경보 및 유실경보율을 충족시킨다. 세 번째 주제는 시변(時變) 오경보를 고려한 리질리언스 주도 설계 방법론이다. 기존의 설계 방법론들은 시스템의 건전성 상태를 시불변(時不變)하다 간주하였으나, 실제 시스템은 운행에 따라 점진적으로 건전성이 저하된다. 본 연구에서는 시변성을 분석하기 위해 시변 오경보율 및 유실경보율에 대한 개념을 새롭게 제안하였으며, 생애주기 시뮬레이션을 통한 총 유지보수 비용 분석 방법론을 개발하였다. 이를 통해 생애주기비용을 보다 엄밀하고 정확하게 추정할 수 있게 되었으며, 이를 최소화하는 방향으로 시스템과 PHM의 설계를 최적화였다. 본 연구에서 제안한 방법론들은 이론적 분석과 사례 연구를 통해 그 효용성을 입증하였다.Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. In order to handle this issue, the resilience-driven system design (RDSD) has been proposed to make engineered systems adaptively reliable by incorporating the prognostics and health management (PHM) method. PHM tracks the health degradation of an engineered system, and provides health state information supporting decisions on condition-based maintenance. Meanwhile, one of the issues awaiting solution in the field of PHM, as well as in RDSD, is to address false alarms. A false alarm is an erroneous report on the health state of an engineered systemChapter 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 Resilience Engineering (Analysis and Design) 7 2.1.1 Resilience Analysis for Mechanical Systems 8 2.1.2 Resilience-Driven System Design (RDSD) for Mechanical Systems 15 2.2 False and Missed Alarms in Prognostics and Health Management 27 2.2.1 Definition of False and Missed Alarms 27 2.2.2 Quantification of False and Missed Alarms 32 2.3 Summary and Discussion 35 Chapter 3. Resilience Analysis Considering False Alarms 37 3.1 Resilience Measure Considering False Alarms 37 3.2 Case Studies 42 3.2.1 Numerical ample 42 3.2.2 Electro-Hydrtatic Actuator (EHA) 44 3.3 Summary and Discussion 53 Chapter 4. Resilience-Driven System Design Considering False Alarms (RDSD-FA) 55 4.1 Overview of RDSD-FA Framework 55 4.2 Resilience Allocation Problem Considering False Alarms 56 4.3 Prognostics and Health Management (PHM) Design Considering False Alarms 60 4.4 Case study: Electro-Hydrostatic Actuator (EHA) 61 4.4.1 Step 1: Resilience Allocation Considering False Alarms 61 4.4.2 Step 2: Reliability-Based Design Optimization 64 4.4.3 Step 3: PHM Design Considering False Alarms 68 4.4.4 Comparison of Design Results from RDSD and RDSD-FA 73 4.5 Summary and Discussion 75 Chapter 5. Resilience-Driven System Design Considering Time-Dependent False Alarms (RDSD-TFA) 77 5.1 Time-Dependent False and Missed Alarms in PHM 79 5.2 Resilience-Driven System Design Considering Time-Dependent False Alarms (RDSD-TFA) 83 5.2.1 Overview of RDSD-TFA Framework 83 5.2.2 Task 1: System Analysis 86 5.2.3 Task 2: PHM Analysis 89 5.2.4 Task 3: Life-Cycle Simulation 91 5.2.5 Task 4: Design Optimization 97 5.3 Case studies 98 5.3.1 Numerical Example of Life-Cycle Simulation 98 5.3.2 Electro-Hydrostatic Actuator (EHA) 107 5.4 Summary and Discussion 123 Chapter 6. Conclusions 126 6.1 Summary and Contributions 126 6.2 Suggestions for Future Research 129 References 132 Appendix 154 Abstract(Korean) 157Docto

Integrating IVHM and Asset Design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collection of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process

Integrating IVHM and asset design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collecting of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process

Architecting Integrated System Health Management for Airworthiness

Integrated System Health Management (ISHM) for Unmanned Aerial Systems (UAS) has been a new area of research - seeking to provide situational awareness to mission and maintenance operations, and for improved decision-making with increased self-autonomy. This research effort developed an analytic architecture and an associated discrete-event simulation using Arena to investigate the potential benefits of ISHM implementation onboard an UAS. The objective of this research is two-fold: firstly, to achieve continued airworthiness by investigating the potential extension of UAS expected lifetime through ISHM implementation, and secondly, to reduce life cycle costs by implementing a Condition-Based Maintenance (CBM) policy with better failure predictions made possible with ISHM. Through a series of design experiments, it was shown that ISHM presented the most cost-effective improvement over baseline systems in situations where the reliability of the UAS is poor (relative to manned systems) and the baseline sensor exhibited poor qualities in terms of missed detection and false alarm rates. From the simulation results of the test scenarios, it was observed that failure occurrence rates, sensor quality characteristics and ISHM performance specifications were significant factors in determining the output responses of the model. The desired outcome of this research seeks to provide potential designers with top-level performance specifications of an ISHM system based on specified airworthiness and maintenance requirements for the envisaged ISHM-enabled UAS

Fault detection in operating helicopter drive train components based on support vector data description

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

Structural Health Monitoring System Trade Space Analysis Tool with Consideration for Crack Growth, Sensor Degradation and a Variable Detection Threshold

Structural Health Monitoring (SHM) systems face many obstacles and gaps that have resulted in the slow implementation in real-world applications. These obstacles include technology performance, implementation issues and a solid business case that justifies the investment in a SHM system. The presentation of a solid business case for the SHM system is a great challenge and arguably is the main factor contributing to the slow implementation of this technology. The research intent of this dissertation is to focus on the business case by providing a tool to aid decision makers. Simulated aging aircraft flight data are used in this effort due to the fact that many aging military aircraft will be flying beyond their initially intended design life. An analytical model was developed to address the business case and the integration of the SHM system into Condition Based Maintenance (CBM). The model aids the calculation of the cost of Life Cycle (LC) events resulting from the implementation of the SHM system on an aging aircraft. In addition, the model captures the events and effect on aircraft availability due to different SHM detection threshold settings and replacement of degraded sensors. The model captures false alarm rates, crack growth, probability of detection, and sensor degradation amongst other parameters. The proposed analytical model is a useful tool that provides the decision makers the confidence to either implement the SHM system on an aging military aircraft or not. Two models were developed; one was the SHM system model with no degradation and the second was the SHM system model with simulated degrading sensors. Three major subcomponents of the SHM model will be the sensor detection component, the crack growth component and the sensor degradation component (second model only)

Decision Support System for Improved Operations, Maintenance, and Safety: a Data-Driven Approach

With industry 4.0, a new era of the industrial revolution with a focus on automation, inter-connectivity, machine learning, and real-time data collection and analysis are emerging. The smart digital technology which includes smart sensors, data acquisition, processing, and control based on big data, machine learning, and Artificial Intelligence (AI) provides boundless opportunities for the end-users to operate their plants under more optimized, reliable, and safer conditions. During an abnormal event in an industrial facility, operators are inundated with information to infer and act. Hence, there is a critical need to develop solutions that assist operators during such critical events. Also, because of the obsolescence challenges of typical industrial control systems, a new paradigm of Open Process Automation (OPA) is emerging. OPA requires a Real-time Operational Technology (OT) services to analyze the data generated by the sensors and control loops to assist the process plant operations by developing applications for advanced computing platforms in open source software platforms. The aim of this research is to highlight the potential applications of big data analytics, machine learning, and AI methods and develop solutions for plant operation, maintenance, process safety and risk management for real industry problems. This research work includes: 1. an alarm management framework integrated with data-driven (Key Performance Indicators) KPIs bench-marking, and a visualization tool is developed to address alarm management challenges; 2. a deep learning-based data-driven process fault detection and diagnosis method on cloud computing to identify abnormal process conditions; and 3. applications such as predictive maintenance, dynamic risk mapping, incident database analysis, application of Natural Language Processing (NLP) for text classification, and barrier assessment for dynamic risk mapping, A unified workflow approach is used to define the data-sources, applicable domains, and develop proposed applications. This work integrates data generated by field instrumentation, expert knowledge with data analytics and AI techniques to provide guidance to the operator or engineer to effectively take proactive decisions through “action-boards”. The robustness of the developed methods and algorithms is validated using real and simulated data sets. The proposed methods and results provide a future road map for any organization to deal with data integration with such applications leading to productive, safer and more reliable operations

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen