Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model

Gas turbine (GT) engines are known for their high availability and reliability and are extensively used for power generation, marine and aero-applications. Maintenance of such complex machines should be done proactively to reduce cost and sustain high availability of the GT. The aim of this paper is to explore the use of autoregressive (AR) models to predict remaining useful life (RUL) of a GT engine. The Turbofan Engine data from NASA benchmark data repository is used as case study. The parametric investigation is performed to check on any effect of changing model parameter on modelling accuracy. Results shows that a single sensory data cannot accurately predict RUL of GT and further research need to be carried out by incorporating multi-sensory data. Furthermore, the predictions made using AR model seems to give highly pessimistic values for RUL of GT

Enhancing Performance and Reducing Emissions in Natural Gas Aspirated Engines through Machine Learning Algorithm

In an era where the global energy landscape is increasingly defined by the dual imperatives of efficiency and sustainability, the natural gas sector stands at a crucial juncture. The engines powering this sector, especially Natural Gas Fired Reciprocating Engines (NGFRE), are well known for their performance as well as considerable emissions, posing a stark challenge to environmental sustainability goals. This thesis addresses this pivotal issue, presenting a machine learning-based solution to optimize NGFRE performance while substantially reducing their environmental footprint. The research is anchored in an experimental framework involving the AJAX DPC-81 engine compressor, evaluated across a spectrum of operational loads from 40% to 75%. The study leverages an extensive array of sensors to collect detailed real-time data on engine performance, emissions, and vibration parameters. Central to the methodology is the strategic adjustment of the Air Management System (AMS), varying air/fuel ratio to explore their impact on engine dynamics and emissions. The study also incorporates a comprehensive vibration analysis, providing critical insights into the engine's operational stability under different load conditions. Machine Learning (ML) techniques, including Linear Regression, Artificial Neural Networks (ANN), and Support Vector Machines (SVM), are integrated with a Programmable Logic Controller (PLC). This integration not only facilitates a nuanced analysis of the collected data but also enables the accurate prediction of engine performance, paving the way for real-time adaptive control systems. The findings of this research are both revealing and impactful. A notable instance is observed at a 40% engine load with a 70% bypass valve opening, where emissions of methane (CH4) plummet by 64%, nitrogen oxides (NOx) by 52%, and Volatile Organic Compounds (VOC) by 50%. This substantial decrease highlights the effectiveness of the ML-driven approach in curbing harmful emissions. Further, the study unveils the manipulation of the bypass valve position can lead to enhanced fuel efficiency and improved engine stability. For example, at a 75% engine load, the research demonstrates that optimal emission reduction is achieved with a mere 10% bypass valve opening, illuminating the delicate interplay between engine load parameters and environmental emissions. In conclusion, the study demonstrates the effectiveness of ML in enhancing NGFRE performance. It sets a foundation for developing intelligent engine systems that can self-adjust for optimal performance and minimal environmental impact, forging a path to a future where the two are seamlessly integrated

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

A physics-based modeling approach for performance monitoring in gas turbine engines

Performance deterioration monitoring is an essential part of the prognostics and health management (PHM) of gas turbine engines (GTEs). This paper proposes a physics-based modeling approach for performance deterioration monitoring with two model-based performance indicators, heat loss index and power deficit index, for GTE PHM applications. A comprehensive nonlinear thermodynamic model for a single shaft GTE is developed to establish the relation between the operating conditions and the cycle parameters. The model, once properly calibrated, is able to predict the GTE cycle parameters in a healthy condition as the baseline, while in reality, the measured parameters gradually deviate from the baseline, which reflects the performance deterioration of the GTE. To represent the degradation level, the heat loss index is defined as the normalized measure of the thermal power that is being wasted in the GTE compared to the healthy condition. Similarly, the power deficit index is defined as the deficiency ratio of the GTE output power due to the performance deterioration. The effectiveness of the performance indicators in monitoring performance deterioration and t