9 research outputs found
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : 곡과λν κΈ°κ³ν곡곡νλΆ, 2021.8. μμ¬μ
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νμ₯μμλ μ£Όλ³μκΈ°μ μμ ν μλμ μν΄ μνλ₯Ό μ ννκ² μ§λ¨ν μ μλ prognostics and health management (PHM)μ κ°μ κΈ°μ μ΄ νμνλ€. μ£Όλ³μκΈ° μ§λ¨μ μν΄ κ°λ°λ λ€μν λ°©λ² μ€ μΈκ³΅μ§λ₯(AI) κΈ°λ° μ κ·Όλ²μ μ°μ
κ³Ό νκ³μμ λ§μ κ΄μ¬μ λ°κ³ μλ€. λμ±μ΄ λ°©λν λ°μ΄ν°μ ν¨κ» λμ μ±λ₯μ λ¬μ±νλ λ₯ λ¬λ κΈ°μ μ μ£Όλ³μκΈ° κ³ μ₯ μ§λ¨μ νμλ€μκ² λμ κ΄μ¬μ κ°κ² ν΄μ€¬λ€. κ·Έ μ΄μ λ λ₯ λ¬λ κΈ°μ μ΄ μμ€ν
μ λλ©μΈ μ§μμ κΉμ΄ μ΄ν΄ν νμ μμ΄ λλμ λ°μ΄ν°λ§ μ£Όμ΄μ§λ€λ©΄ 볡μ‘ν μμ€ν
μ΄λΌλ μ¬μ©μμ λͺ©μ μ λ§κ² κ·Έ ν΄λ΅μ μ°Ύμ μ μκΈ° λλ¬Έμ λ₯ λ¬λμ λν κ΄μ¬μ μ£Όλ³μκΈ° κ³ μ₯ μ§λ¨ λΆμΌμμ νΉν λλλ¬μ‘λ€.
κ·Έλ¬λ, μ΄λ¬ν λ°μ΄λ μ§λ¨ μ±λ₯μ μμ§ μ€μ μ£Όλ³μκΈ° μ°μ
μμλ λ§μ κ΄μ¬μ μ»κ³ μμ§λ λͺ»ν κ²μΌλ‘ μλ €μ‘λ€. κ·Έ μ΄μ λ μ°μ
νμ₯μ λΉνμ§λ°μ΄ν°μ μλμ κ³ μ₯λ°μ΄ν° λλ¬Έμ μ°μν λ₯λ¬λκΈ°λ°μ κ³ μ₯ μ§λ¨ λͺ¨λΈλ€μ κ°λ°νκΈ° μ΄λ ΅λ€.
λ°λΌμ λ³Έ νμλ
Όλ¬Έμμλ μ£Όλ³μκΈ° μ°μ
μμ νμ¬ λλλκ³ μλ μΈκ°μ§ μ΄μλ₯Ό μ°κ΅¬νμλ€. 1) 건μ μ± νλ©΄ μκ°ν μ΄μ, 2) λ°μ΄ν° λΆμ‘± μ΄μ, 3) μ¬κ°λ μ΄μ λ€μ 극볡νκΈ° μν λ₯ λ¬λ κΈ°λ° κ³ μ₯ μ§λ¨ μ°κ΅¬λ₯Ό μ§ννμλ€. μκ°λ μΈκ°μ§ μ΄μλ€μ κ°μ νκΈ° μν΄ λ³Έ νμλ
Όλ¬Έμ μΈ κ°μ§ μ°κ΅¬λ₯Ό μ μνμλ€.
첫 λ²μ§Έ μ°κ΅¬λ 보쑰 κ°μ§ μμ
μ΄ μλ μ€μ§λ μλ μΈμ½λλ₯Ό ν΅ν΄ 건μ μ± νλ©΄μ μ μνμλ€. μ μλ λ°©λ²μ λ³μκΈ° μ΄ν νΉμ±μ μκ°ν ν μ μλ€. λν, μ€μ§λ μ κ·Όλ²μ νμ©νκΈ° λλ¬Έμ λ°©λν λΉνμ§λ°μ΄ν° κ·Έλ¦¬κ³ μμμ νμ§λ°μ΄ν°λ§μΌλ‘ ꡬνλ μ μλ€. μ μλ°©λ²μ μ£Όλ³μκΈ° 건μ μ±μ 건μ μ± νλ©΄κ³Ό ν¨κ» μκ°ννκ³ , λ§€μ° μ μ μμμ λ μ΄λΈ λ°μ΄ν°λ§μΌλ‘ μ£Όλ³μκΈ° κ³ μ₯μ μ§λ¨νλ€.
λ λ²μ§Έ μ°κ΅¬λ κ·μΉ κΈ°λ° Duval λ°©λ²μ AI κΈ°λ° deep neural network (DNN)κ³Ό μ΅ν©(bridge)νλ μλ‘μ΄ νλ μμν¬λ₯Ό μ μνμλ€. μ΄ λ°©λ²μ λ£°κΈ°λ°μ Duvalμ μ¬μ©νμ¬ λΉνμ§λ°μ΄ν°λ₯Ό μλ λ μ΄λΈλ§νλ€ (pseudo-labeling). λν, AI κΈ°λ° DNNμ μ κ·ν κΈ°μ κ³Ό λ§€κ° λ³μ μ μ΄ νμ΅μ μ μ©νμ¬ λ
Έμ΄μ¦κ° μλ pseudo-label λ°μ΄ν°λ₯Ό νμ΅νλλ° μ¬μ©λλ€. κ°λ°λ κΈ°μ μ λ°©λνμμ λΉνμ§λ°μ΄ν°λ₯Ό λ£°κΈ°λ°μΌλ‘ μΌμ°¨μ μΌλ‘ μ§λ¨ν κ²°κ³Όμ μμμ μ€μ κ³ μ₯λ°μ΄ν°μ ν¨κ» νμ΅λ°μ΄ν°λ‘ νλ ¨νμμ λ κΈ°μ‘΄μ μ§λ¨ λ°©λ²λ³΄λ€ νκΈ°μ μΈ ν₯μμ κ°λ₯μΌ νλ€.
λμΌλ‘, μΈ λ²μ§Έ μ°κ΅¬λ κ³ μ₯ νμ
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κ³Ό μ¬κ°λ μ¬μ΄μλ λΆκ· μΌν λ°μ΄ν° λΆν¬λ‘ μ΄λ£¨μ΄μ Έ μλ€. κ·Έ μ΄μ λ μ¬κ°λμ κ²½μ° λ μ΄λΈλ§μ΄ νμ λμ΄ μμ§λ§ κ³ μ₯ νμ
μ κ²½μ°λ μ€μ μ£Όλ³μκΈ°λ‘λΆν° κ³ μ₯ νμ
λ°μ΄ν°λ₯Ό μ»κΈ°κ° λ§€μ° μ΄λ ΅κΈ° λλ¬Έμ΄λ€. λ°λΌμ, λ³Έ μ°κ΅¬μμ μΈλ²μ§Έλ‘ κ°λ°ν κΈ°μ μ μ€λλ λ°μ΄ν° μμ±μ λ§€μ° μ°μν μ±λ₯μ λ¬μ±νκ³ μλ generative adversarial network (GAN)λ₯Ό ν΅ν΄ λΆκ· νν λ μνλ₯Ό κ· μΌν μμ
μ μννλ λμμ κ³ μ₯ λͺ¨λμ μ¬κ°λλ₯Ό μ§λ¨νλ λͺ¨λΈμ κ°λ°νμλ€.Due to the rapid development and advancement of todayβs industry, the demand for safe and reliable power distribution and transmission lines is becoming more critical; thus, prognostics and health management (hereafter, PHM) is becoming more important in the power transformer industry. Among various methods developed for power transformer diagnosis, the artificial intelligence (AI) based approach has received considerable interest from academics. Specifically, deep learning technology, which offers excellent performance when used with vast amounts of data, is also rapidly gaining the spotlight in the academic field of transformer fault diagnosis. The interest in deep learning has been especially noticed in the field of fault diagnosis, because deep learning algorithms can be applied to complex systems that have large amounts of data, without the need for a deep understanding of the domain knowledge of the system.
However, the outstanding performance of these diagnosis methods has not yet gained much attention in the power transformer PHM industry. The reason is that a large amount of unlabeled and a small amount of fault data always restrict their deep-learning-based diagnosis methods in the power transformer PHM industry.
Therefore, in this dissertation research, deep-learning-based fault diagnosis methods are developed to overcome three issues that currently prevent this type of diagnosis in industrial power transformers: 1) the visualization of health feature space issue, 2) the insufficient data issue, and 3) the severity issue. To cope with these challenges, this thesis is composed of three research thrusts. The first research thrust develops a health feature space via a semi-supervised autoencoder with an auxiliary detection task. The proposed method can visualize a monotonic health trendability of the transformerβs degradation properties. Further, thanks to the use of a semi-supervised approach, the method is applicable to situations with a large amount of unlabeled and a small amount labeled data (a situation common in industrial datasets). Next, the second research thrust proposes a new framework, that bridges the rule-based Duval method with an AI-based deep neural network (BDD). In this method, the rule-based Duval method is utilized to pseudo-label a large amount of unlabeled data. Furthermore, the AI-based DNN is used to apply regularization techniques and parameter transfer learning to learn the noisy pseudo-labelled data. Finally, the third thrust not only identifies fault types but also indicates a severity level. However, the balance between labeled fault types and the severity level is imbalanced in real-world data. Therefore, in the proposed method, diagnosis of fault types β with severity levels β under imbalanced conditions is addressed by utilizing a generative adversarial network with an auxiliary classifier. The validity of the proposed methods is demonstrated by studying massive unlabeled dissolved gas analysis (DGA) data, provided by the Korea Electric Power Company (KEPCO), and sparse labeled data, provided by the IEC TC 10 database. Each developed method could be used in industrial fields that use power transformers to monitor the health feature space, consider severity level, and diagnose transformer faults under extremely insufficient labeled fault data.Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Research Scope and Overview 4
1.3 Dissertation Layout 7
Chapter 2 Literature Review 9
2.1 A Brief Overview of Rule-Based Fault Diagnosis 9
2.2 A Brief Overview of Conventional AI-Based Fault Diagnosis 11
Chapter 3 Extracting Health Feature Space via Semi-Supervised Autoencoder with an Auxiliary Task (SAAT) 13
3.1 Backgrounds of Semi-supervised autoencoder (SSAE) 15
3.1.1 Autoencoder: Unsupervised Feature Extraction 15
3.1.2 Softmax Classifier: Supervised Classification 17
3.1.3 Semi-supervised Autoencoder 18
3.2 Input DGA Data Preprocessing 20
3.3 SAAT-Based Fault Diagnosis Method 21
3.3.1 Roles of the Auxiliary Detection Task 23
3.3.2 Architecture of the Proposed SAAT 27
3.3.3 Health Feature Space Visualization 29
3.3.4 Overall Procedure of the Proposed SAAT-based Fault Diagnosis 30
3.4 Performance Evaluation of SAAT 31
3.4.1 Data Description and Implementation 31
3.4.2 An Outline of Four Comparative Studies and Quantitative Evaluation Metrics 33
3.4.3 Experimental Results and Discussion 36
3.5 Summary and Discussion 49
Chapter 4 Learning from Even a Weak Teacher: Bridging Rule-based Duval Weak Supervision and a Deep Neural Network (BDD) for Diagnosing Transformer 51
4.1 Backgrounds of BDD 53
4.1.1 Rule-based method: Duval Method 53
4.1.2 Deep learning Based Method: Deep Neural Network 54
4.1.3 Parameter Transfer 55
4.2 BDD Based Fault Diagnosis 56
4.2.1 Problem Statement 56
4.2.2 Framework of the Proposed BDD 57
4.2.3 Overall Procedure of BDD-based Fault Diagnosis 63
4.3 Performance Evaluation of the BDD 64
4.3.1 Description of Data and the DNN Architecture 64
4.3.2 Experimental Results and Discussion 66
4.4 Summary and Discussion 76
Chapter 5 Generative Adversarial Network with Embedding Severity DGA Level 79
5.1 Backgrounds of Generative Adversarial Network 81
5.2 GANES based Fault Diagnosis 82
5.2.1 Training Strategy of GANES 82
5.2.2 Overall procedure of GANES 87
5.3 Performance Evaluation of GANES 91
5.3.1 Description of Data 91
5.3.2 Outlines of Experiments 91
5.3.3 Preliminary Experimental Results of Various GANs 95
5.3.4 Experiments for the Effectiveness of Embedding Severity DGA Level 99
5.4 Summary and Discussion 105
Chapter 6 Conclusion 106
6.1 Contributions and Significance 106
6.2 Suggestions for Future Research 108
References 110
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