A review on prognostics and health monitoring of proton exchange membrane fuel cell

Fuel cell technology can be traced back to 1839 when British scientist Sir William Grove discovered that it was possible to generate electricity by the reaction between hydrogen and oxygen gases. However, fuel cell still cannot compete with internal combustion engines although they have many advantages including zero carbon emissions. Fossil fuels are cheaper and present very high volumetric energy densities compared with the hydrogen gas. Furthermore, hydrogen storage as a liquid is still a huge challenge. Another important disadvantage is the lifespan of the fuel cell because of their durability, reliability and maintainability. Prognostics is an emerging technology in sustainability of engineering systems through failure prevention, reliability assessment and remaining useful lifetime estimation. Prognostics and health monitoring can play a critical role in enhancing the durability, reliability and maintainability of the fuel cell system. This paper presents a review on the current state-of-the-art in prognostics and health monitoring of Proton Exchange Membrane Fuel Cell (PEMFC), aiming at identifying research and development opportunities in these fields. This paper also highlights the importance of incorporating prognostics and failure modes, mechanisms and effects analysis (FMMEA) in PEMFC to give them sustainable competitive advantage when compared with other non-clean energy solutions

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

A framework development to predict remaining useful life of a gas turbine mechanical component

Power-by-the-hour is a performance based offering for delivering outstanding service to operators of civil aviation aircraft. Operators need to guarantee to minimise downtime, reduce service cost and ensure value for money which requires an innovative advanced technology for predictive maintenance. Predictability, availability and reliability of the engine offers better service for operators, and the need to estimate the expected component failure prior to failure occurrence requires a proactive approach to predict the remaining useful life of components within an assembly. This research offers a framework for component remaining useful life prediction using assembly level data. The thesis presents a critical analysis on literature identifying the Weibull method, statistical technique and data-driven methodology relating to remaining useful life prediction, which are used in this research. The AS-IS practice captures relevant information based on the investigation conducted in the aerospace industry. The analysis of maintenance cycles relates to the examination of high-level events for engine availability, whereby more communications with industry showcase a through-life performance timeline visualisation. Overhaul sequence and activities are presented to gain insights of the timeline visualisation. The thesis covers the framework development and application to gas turbine single stage assembly, repair and replacement of components in single stage assembly, and multiple stage assembly. The framework is demonstrated in aerospace engines and power generation engines. The framework developed enables and supports domain experts to quickly respond to, and prepare for maintenance and on-time delivery of spare parts. The results of the framework show the probability of failure based on a pair of error values using the corresponding Scale and Shape parameters. The probability of failure is transformed into the remaining useful life depicting a typical Weibull distribution. The resulting Weibull curves developed with three scenarios of the case shows there are components renewals, therefore, the remaining useful life of the components are established. The framework is validated and verified through a case study with three scenarios and also through expert judgement

Enhancing fuel cell lifetime performance through effective health management

Hydrogen fuel cells, and notably the polymer electrolyte fuel cell (PEFC), present an important opportunity to reduce greenhouse gas emissions within a range of sectors of society, particularly for transportation and portable products. Despite several decades of research and development, there exist three main hurdles to full commercialisation; namely infrastructure, costs, and durability. This thesis considers the latter of these. The lifetime target for an automotive fuel cell power plant is to survive 5000 hours of usage before significant performance loss; current demonstration projects have only accomplished half of this target, often due to PEFC stack component degradation. Health management techniques have been identified as an opportunity to overcome the durability limitations. By monitoring the PEFC for faulty operation, it is hoped that control actions can be made to restore or maintain performance, and achieve the desired lifetime durability. This thesis presents fault detection and diagnosis approaches with the goal of isolating a range of component degradation modes from within the PEFC construction. Fault detection is achieved through residual analysis against an electrochemical model of healthy stack condition. An expert knowledge-based diagnostic approach is developed for fault isolation. This analysis is enabled through fuzzy logic calculations, which allows for computational reasoning against linguistic terminology and expert understanding of degradation phenomena. An experimental test bench has been utilised to test the health management processes, and demonstrate functionality. Through different steady-state and dynamic loading conditions, including a simulation of automotive application, diagnosis results can be observed for PEFC degradation cases. This research contributes to the areas of reliability analysis and health management of PEFC fuel cells. Established PEFC models have been updated to represent more accurately an application PEFC. The fuzzy logic knowledge-based diagnostic is the greatest novel contribution, with no examples of this application in the literature

Investigation into the performance and acoustical characteristics of proton exchange membrane fuel cells

Over the last three decades, much research has been conducted into developing fuel cells (FCs) owing to their high efficiency and environmental friendly operation. Among different types of FCs, proton exchange membrane fuel cells (PEMFCs) are popular for stationary and mobile applications. They have a high-energy density, low operating temperatures, quick start-up times and zero emissions. However, their low reliability and unacceptable high costs limit their wider adoption in the above-mentioned applications. Lack of understanding and complexity of FC operations, mechanical failure, and the lack of root cause analysis and prevention techniques are obstacles that stand in the way of improving such low durability and reliability. The aim of this PhD work is first to derive a realistic model that represents the complex operations of a single PEMFC and experimentally verify the effectiveness of the developed model. Second, to gain a clear understanding of PEMFCs’ failure modes and effects analysis. Third, to assess the detectability of commonly used monitoring techniques and explore the acoustical characteristics of PEMFCs under normal and faulty conditions. Power parameters are directly affecting the operating conditions of PEMFC and hence are expected to carry useful information about their conditions. Unfortunately, those measurements are intrusive and they do not detect faults at the early stages of onset. However, PEMFCs are dynamic chemical systems that involve phase transitions and thus are acoustically active. Chemical changes during interactions are usually accompanied by a transfer of energy and part of energy may be converted to an acoustic emission (AE). Although, AE techniques are widely adopted for monitoring chemical and electrochemical systems, no rigorous work has undertaken to characterise the acoustical behaviour of PEMFCs. Therefore, the nature and source of AE in PEMFCs are identified and effect of load variations on them are experimentally investigated as part of this study. It is anticipated the work presented in this thesis will open the door for more studies to build non-intrusive robust diagnostic systems, which will contribute to enhance the reliability of PEMFCs

고분자 전해질막 연료전지 시스템 고장 반응 및 심각도 기반 고장진단 방법

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부(멀티스케일 기계설계전공), 2021.8. 박진영.최근 들어 지속 가능하며 오염 없는 수소 사회에 대한 관심이 증가하고 있다. 수소는 우주에 가장 많은 물질이며 또한 쉽게 제조할 수 있다. 친환경 기술 개발과 더불어 수소 사회가 실현 된다는 가정하에, 수소에너지를 전기에너지로 변환시켜 주는 장치가 반드시 필요하다. 고분자 전해질막 연료전지 시스템은 산소와 수소의 전기화학반응을 이용하여 전기를 발전시키는 시스템이며, 다른 변환 장치에 비해 많은 장점을 가지고 있다. 또한, 가장 널리 사용되고 있는 장치이기도 하다. 다만, 연료전지의 상용화와 보급에 있어 내구성과 신뢰성은 아직 부족하여 극복해야 할 문제로 언급되고 있다. 이러한 내구성과 신뢰성 증진을 위해서는 고장 진단 기술이 반드시 필요하다. 연료전지는 운전 조건에 따라 그 성능과 내구성이 크게 영향을 받기 때문에 시스템에 발생한 문제를 빠르게 진단하여 장치를 보호하는 것이 중요하기 때문이다. 본 연구에서는 먼저 연료전지 시스템에 고장 발생시 그 영향을 관찰하였다. 일차적으로는 연료전지 스택에 반응의 공급 혹은 냉각이 원활히 이루어지지 않는 상황에서의 변화를 실험적으로 관찰하였다. 이어, 연료전지 시스템을 제작하여 연료 공급 시스템, 공기 공급 시스템, 열 관리 시스템에서 발생할 수 잇는 고장 시나리오를 설정하였다. 고장 시나리오는 연료전지 스택 혹은 시스템 전체에 미칠 수 있는 영향을 그 심각도에 따라 분류하였다. 마지막으로 고장을 인가하고 제어 및 계측 신호의 변화 양상을 관찰 및 분석하였다. 다음으로, 본 연구에서는 이러한 연료전지 시스템 고장을 진단할 수 있는 방법을 제안하였다. 고장의 심각도에 따라 변화 반응의 크기와 속도가 다르다는 점에 착안하여, 치명적 고장, 심각한 고장, 사소한 고장을 각각 진단하는 뉴럴 네트워크 기반 알고리즘을 개발하고, 이를 심각도 기반 고장 진단 알고리즘이라 명명하였다. 각각의 뉴럴 네트워크에 입력하는 계측 잔차 값의 이동 평균 시간과 이를 나누는 분산의 배수 값을 조절함으로 서, 진단 알고리즘의 민감도와 강건성을 동시에 달성할 수 있다는 장점을 갖는다. 또한 고장 실험 없이 고장 시 예상되는 제어 및 계측 신호 값의 증감을 테이블화 하는 것만으로도 본 알고리즘을 개발할 수 있다는 장점을 갖는다. 이러한 방법으로 개발 된 심각도 기반 고장 진단 알고리즘에 고장실험 데이터를 입력한 결과 고장을 성공적으로 진단하는 것을 확인하였다. 이어서, 본 연구에서는 내부 전류 분포를 예측할 수 있는 모델 개발법을 제안하였다. 지금까지의 연료전지 내부 전류 분포 연구는 실험 기반으로, 혹은 모델기반으로 각각 이루어져 왔다. 다만 두가지 접근 방법 모두 필연적으로 한계점을 가진다. 이러한 한계를 뉴럴 네트워크를 도입하여 극복하고자 하였다. 분할 연료전지를 이용하여 압력, 온도, 유량 및 가습도가 변하는 다양한 운전 조건에서의 전류 분포 정보를 습득하였고, 이를 뉴럴 네트워크 모델에 학습시켰다. 그 결과 제한된 데이터만으로 모델을 개발하고, 다양한 운전조건에서 전류 밀도 분포를 예측 할 수 있음을 확인하였다. 이러한 접근법은 상용 연료전지의 개발 과정에서 효율적으로 활용될 것이라 생각한다. 마지막으로, 본 연구에서는 열화 및 고장 발생에 따른 내부 전류 분포를 예측할 수 있는 모델 개발법을 제안하고 검증하였다. 연료전지는 시간이 지나면 필연적으로 열화 한다는 특성을 가지고 있다. 따라서 열화에 따른 전류 분포 변화 특성을 이해하고 예측하는 작업은 중요하다. 하여, 가속 열화 기법을 도입하여 열화에 따른 내부 전류 분포 변화를 먼저 관찰하였다. 또한 가속 열화 시험 중간에 운전 온도 상승, 당량비 증감, 가습도 저하와 같은 고장을 인가하여 전류 분포 변화 정보를 추가적으로 습득하였다. 이러한 정보를 바탕으로 열화 및 열화 진행 상태에서의 고장 발생 시, 전류밀도 분포를 예측하는 뉴럴 네트워크 기반 모델을 개발하였다. 그 결과 모델을 이용하여 효율적이면서도 정확한 전류 밀도 분포 예측이 가능함을 확인하였다.In recent years, interest in hydrogen society has grown from the viewpoint of a sustainable clean energy society. Hydrogen is the most abundant element in the universe and can be easily produced. When hydrogen becomes a commonly used fuel, an energy conversion device is needed. A polymer electrolyte membrane fuel cell (PEMFC) system is the most widely distributed device so far, with many advantages among many devices. However, there still are some barriers to overcome for the commercialization of the PEMFC system; reliability and durability. In order to improve the reliability and durability of the fuel cell system, fault diagnosis technology is essentially required. Since the performance and durability of the PFMFC highly depend on operating conditions, faults in the system should be correctly detected in the early stage for its protection. Firstly, fault responses of a PEMFC stack and PEMFC system are investigated in this study. A response of 1 kW PEMFC stack under insufficient reactant supply or failure thermal management is investigated. Next, probable fault scenarios in a 1 kW class PEMFC system are established. The fault scenarios in air providing system, fuel providing system and thermal management system are classified depending on their fault severity to the stack or the entire system. Responses of control and sensing signals are investigated and analyzed under each fault scenario. Secondly, a fault diagnostic method for the PEMFC system is suggested in this study. Considering that response time and magnitude differ depending on fault severity, three neural networks that diagnose the critical fault, significant fault and minor fault, respectively, are developed. The neural networks together work as a severity-based fault diagnosis algorithm. The algorithm can achieve both sensitivity and robustness by adjusting the moving average time and standard deviation multiplication value that divides the residual data. The residual data is acquired from the control and sensing signals during the system operation. The severity-based fault diagnosis algorithm can be developed using a tabularized expected fault response without experimental data. As a result, the developed algorithm successfully diagnosed all the considered fault scenarios. Thirdly, a local current distribution prediction method is suggested in this study. Local current distribution studies have been conducted experimentally or numerically. Both approaches had limitations. In order to overcome the limitations, a neural network-based local current distribution prediction model is developed. Current distribution data is collected under various pressure, temperature, reactant stoichiometric ratio and relative humidity conditions. The model is developed with the data and successfully predicted local current distribution. Using the model, the effect of the operating parameters is investigated. Lastly, a local current distribution prediction model under degradation and fault is suggested in this study. The performance of the fuel cell inevitably decreases over time. With the degradation, local current distribution also changes. Therefore, understanding and predicting the current distribution changes are important. An accelerated stress test (AST) is applied to the fuel cell for fast degradation. With the AST, current distribution data is collected. Also, fault data under elevated temperature, reduced humidity and varying cathode stoichiometric ratio condition are collected. With the collected data, local current distribution model based on a neural network is developed. As a result, the model predicted the current distribution under degradation and fault with high accuracy. In summary, a fault response of PEMFC is investigated from the viewpoint of the system and local current distribution. A severity-based fault diagnosis algorithm is suggested and validated with the PEMFC system fault experimental data. Also, local current distribution prediction algorithm is suggested and successively predicted the current distribution under PEMFC degradation and faults.Chapter 1. Introduction 1 1.1 Background of study 1 1.2 Literature survey 7 1.2.1 PEMFC fault diagnosis technology 7 1.2.2 PEMFC local current distribution 11 1.3 Objective and scopes 19 Chapter 2. Fault response of PEMFC system 22 2.1 Introduction 22 2.2 Fault response of 1 kW stack 23 2.2.1 Experimental setup description 23 2.2.2 Fault response of stack 29 2.3 Fault response of 1 kW PEMFC system 34 2.3.1 PEMFC system description 34 2.3.2 Fault scenarios 44 2.3.3 Fault response of PEMFC system 52 2.4 Summary 63 Chapter 3. Severity-based fault diagnostic method for PEMFC system 64 3.1 Introduction 64 3.2 Fault residual patterns 68 3.2.1 Input values 68 3.2.2 Normal state 69 3.2.3 Faul residual pattern table 72 3.3 Fault diagnosis algorithm development 79 3.3.1 Severity-based fault diagnosis concept 79 3.3.2 Algorithm development 82 3.4 Results and discussion 88 3.5 Summary 105 Chapter 4. Current distribution prediction with neural network 106 4.1 Introduction 106 4.2 Experimental setup 108 4.2.1 Experimental apparatus 108 4.2.2 Experimental conditions 113 4.3 Model development 116 4.3.1 Neural network model 116 4.3.2 Data conditioning 119 4.3.3 Model training 122 4.4 Results and discussion 127 4.4.1 Model accuracy 127 4.4.2 Effects of parameters on current distribution 129 4.4.3 Effects of parameters on standard deviations 133 4.4.3 Uniform current distribution 134 4.5 Summary 138 Chapter 5. Current distribution prediction under degradation and fault 139 5.1 Introduction 139 5.2 Accelerated stress test 140 5.3 Experimental setup 143 5.3.1 Experimental apparatus 143 5.3.2 Experimental conditions 148 5.4 Current distribution characteristics 152 5.4.1 Local current distribution change with accelerated stress test 152 5.4.2 Local current distribution change under faults 156 5.5 Model development 161 5.5.1 Neural network models 161 5.5.2 Data conditioning 166 5.5.3 Model training 169 5.6 Prediction results 171 5.7 Summary 176 Chapter 6. Concluding remarks 177 References 180 Abstract (in Korean) 197박

Contribution au pronostic d'une pile à combustible de type PEMFC - approche par filtrage particulaire.

The development of new energy converters, more efficient and environment friendly, such as fuelcells, tends to accelerate. Nevertheless, their large scale diffusion supposes some guaranties in termsof safety and availability. A possible solution to do so is to develop Prognostics and HealthManagement (PHM) on these systems, in order to monitor and anticipate the failures, and torecommend the necessary actions to extend their lifetime. In this spirit, this thesis deals with theproposal of a prognostics approach based on particle filtering dedicated to PEMFCs.The reasoning focuses first on setting a formalization of the working framework and theexpectations. This is pursued by the development of a physic-based modelling enabling a state ofhealth estimation and its evolution in time. The state estimation is made thanks to particle filtering.Different variants of filters are considered on the basis of the literature and new proposals adaptedto PHM are proposed and compared to existing ones. State of health estimates given by the filter areused to predict the future state of the system and its remaining useful life. All the proposals arevalidated on four datasets from PEMFC following different mission profiles. The results show goodperformances for predictions and remaining useful life estimates before failure.Le développement de nouveaux convertisseurs d’énergie, plus efficients et plus respectueux del’environnement, tels que les piles à combustibles, tend à s’accélérer. Leur diffusion à grande échellesuppose cependant des garanties en termes de sécurité et de disponibilité. Une solution possiblepour ce faire est de développer des solutions de Prognostics and Health Management (PHM) de cessystèmes, afin de mieux les surveiller, anticiper les défaillances et recommander les actionsnécessaires à l’allongement de leur durée de vie. Dans cet esprit, cette thèse porte sur la propositiond’une approche de pronostic dédiée aux piles à combustibles de types PEMFC à l’aide de filtrageparticulaire.Le raisonnement s’attache tout d’abord à mettre en place une formalisation du cadre de travailainsi que des exigences de mise en. Ceci se poursuit par le développement d’un modèle basé sur laphysique permettant une estimation d’état de santé et de son évolution temporelle. L’estimationd’état est réalisée grâce à du filtrage particulaire. Différentes variantes de filtres sont considérées surla base d’une de la littérature et de nouvelles propositions adaptées au PHM sont formulées etcomparées à celles existantes. Les estimations d’état de santé fournies par le processus de filtragesont utilisées pour réaliser des prédictions de l’état de santé futur du système, puis de sa durée devie résiduelle. L’ensemble des propositions est validé sur 4 jeux de données obtenus sur des PEMFCsuivant des profils de mission variés. Les résultats montrent de bonnes performances deprédictions et d’estimations de durée de vie résiduelle avant défaillance

Data Requirements to Enable PHM for Liquid Hydrogen Storage Systems from a Risk Assessment Perspective

Quantitative Risk Assessment (QRA) aids the development of risk-informed safety codes and standards which are employed to reduce risk in a variety of complex technologies, such as hydrogen systems. Currently, the lack of reliability data limits the use of QRAs for fueling stations equipped with bulk liquid hydrogen storage systems. In turn, this hinders the ability to develop the necessary rigorous safety codes and standards to allow worldwide deployment of these stations. Prognostics and Health Management (PHM) and the analysis of condition-monitoring data emerge as an alternative to support risk assessment methods. Through the QRA-based analysis of a liquid hydrogen storage system, the core elements for the design of a data-driven PHM framework are addressed from a risk perspective. This work focuses on identifying the data collection requirements to strengthen current risk analyses and enable data-driven approaches to improve the safety and risk assessment of a liquid hydrogen fueling infrastructure