PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques

© IFAC 2014. This work is posted here by permission of IFAC for your personal use. Not for distribution. The original version was published in ifac-papersonline.netIn this work, a fault diagnosis methodology termed VisualBlock-Fuzzy Inductive Reasoning, i.e. VisualBlock-FIR, based on fuzzy and pattern recognition approaches is presented and applied to PEM fuel cell power systems. The innovation of this methodology is based on the hybridization of an artificial intelligence methodology that combines fuzzy approaches with well known pattern recognition techniques. To illustrate the potentiality of VisualBlock-FIR, a non-linear fuel cell simulator that has been proposed in the literature is employed. This simulator includes a set of five fault scenarios with some of the most frequent faults in fuel cell systems. The fault detection and identification results obtained for these scenarios are presented in this paper. It is remarkable that the proposed methodology compares favorably to the model-based methodology based on computing residuals while detecting and identifying all the proposed faults much more rapidly. Moreover, the robustness of the hybrid fault diagnosis methodology is also studied, showing good behavior even with a level of noise of 20 dB.Peer ReviewedPostprint (published version

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

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