9 research outputs found

    Semi-Supervised Fine-Tuning for Deep Learning Models in Remote Sensing Applications

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    A combinatory approach of two well-known fields: deep learning and semi supervised learning is presented, to tackle the land cover identification problem. The proposed methodology demonstrates the impact on the performance of deep learning models, when SSL approaches are used as performance functions during training. Obtained results, at pixel level segmentation tasks over orthoimages, suggest that SSL enhanced loss functions can be beneficial in models' performance

    Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches

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    Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy

    Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

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    Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method

    λΉ„ν‘œμ§€ κ³ μž₯ 데이터와 μœ μ€‘κ°€μŠ€λΆ„μ„λ°μ΄ν„°λ₯Ό μ΄μš©ν•œ λ”₯λŸ¬λ‹κΈ°λ°˜ μ£Όλ³€μ••κΈ° κ³ μž₯진단 연ꡬ

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 기계항곡곡학뢀, 2021.8. μ†Œμž¬μ›….μ˜€λŠ˜λ‚  μ‚°μ—…μ˜ κΈ‰μ†ν•œ λ°œμ „κ³Ό κ³ λ„ν™”λ‘œ 인해 μ•ˆμ „ν•˜κ³  μ‹ λ’°ν•  수 μžˆλŠ” μ „λ ₯ 계톡에 λŒ€ν•œ μˆ˜μš”λŠ” λ”μš± μ€‘μš”ν•΄μ§€κ³  μžˆλ‹€. λ”°λΌμ„œ μ‹€μ œ μ‚°μ—… ν˜„μž₯μ—μ„œλŠ” μ£Όλ³€μ••κΈ°μ˜ μ•ˆμ „ν•œ μž‘λ™μ„ μœ„ν•΄ μƒνƒœλ₯Ό μ •ν™•ν•˜κ²Œ 진단할 수 μžˆλŠ” prognostics and health management (PHM)와 같은 기술이 ν•„μš”ν•˜λ‹€. μ£Όλ³€μ••κΈ° 진단을 μœ„ν•΄ 개발된 λ‹€μ–‘ν•œ 방법 쀑 인곡지λŠ₯(AI) 기반 접근법은 μ‚°μ—…κ³Ό ν•™κ³„μ—μ„œ λ§Žμ€ 관심을 λ°›κ³  μžˆλ‹€. λ”μš±μ΄ λ°©λŒ€ν•œ 데이터와 ν•¨κ»˜ 높은 μ„±λŠ₯을 λ‹¬μ„±ν•˜λŠ” λ”₯ λŸ¬λ‹ κΈ°μˆ μ€ μ£Όλ³€μ••κΈ° κ³ μž₯ μ§„λ‹¨μ˜ ν•™μžλ“€μ—κ²Œ 높은 관심을 κ°–κ²Œ 해쀬닀. κ·Έ μ΄μœ λŠ” λ”₯ λŸ¬λ‹ 기술이 μ‹œμŠ€ν…œμ˜ 도메인 지식을 깊이 이해할 ν•„μš” 없이 λŒ€λŸ‰μ˜ λ°μ΄ν„°λ§Œ 주어진닀면 λ³΅μž‘ν•œ μ‹œμŠ€ν…œμ΄λΌλ„ μ‚¬μš©μžμ˜ λͺ©μ μ— 맞게 κ·Έ 해닡을 찾을 수 있기 λ•Œλ¬Έμ— λ”₯ λŸ¬λ‹μ— λŒ€ν•œ 관심은 μ£Όλ³€μ••κΈ° κ³ μž₯ 진단 λΆ„μ•Όμ—μ„œ 특히 λ‘λ“œλŸ¬μ‘Œλ‹€. κ·ΈλŸ¬λ‚˜, μ΄λŸ¬ν•œ λ›°μ–΄λ‚œ 진단 μ„±λŠ₯은 아직 μ‹€μ œ μ£Όλ³€μ••κΈ° μ‚°μ—…μ—μ„œλŠ” λ§Žμ€ 관심을 μ–»κ³  μžˆμ§€λŠ” λͺ»ν•œ κ²ƒμœΌλ‘œ μ•Œλ €μ‘Œλ‹€. κ·Έ μ΄μœ λŠ” μ‚°μ—…ν˜„μž₯의 λΉ„ν‘œμ§€λ°μ΄ν„°μ™€ μ†ŒλŸ‰μ˜ κ³ μž₯데이터 λ•Œλ¬Έμ— μš°μˆ˜ν•œ λ”₯λŸ¬λ‹κΈ°λ°˜μ˜ κ³ μž₯ 진단 λͺ¨λΈλ“€μ„ κ°œλ°œν•˜κΈ° μ–΄λ ΅λ‹€. λ”°λΌμ„œ λ³Έ ν•™μœ„λ…Όλ¬Έμ—μ„œλŠ” μ£Όλ³€μ••κΈ° μ‚°μ—…μ—μ„œ ν˜„μž¬ λŒ€λ‘λ˜κ³  μžˆλŠ” 세가지 이슈λ₯Ό μ—°κ΅¬ν•˜μ˜€λ‹€. 1) 건전성 평면 μ‹œκ°ν™” 이슈, 2) 데이터 λΆ€μ‘± 이슈, 3) 심각도 이슈 듀을 κ·Ήλ³΅ν•˜κΈ° μœ„ν•œ λ”₯ λŸ¬λ‹ 기반 κ³ μž₯ 진단 연ꡬλ₯Ό μ§„ν–‰ν•˜μ˜€λ‹€. μ†Œκ°œλœ 세가지 μ΄μŠˆλ“€μ„ κ°œμ„ ν•˜κΈ° μœ„ν•΄ λ³Έ ν•™μœ„λ…Όλ¬Έμ€ μ„Έ 가지 연ꡬλ₯Ό μ œμ•ˆν•˜μ˜€λ‹€. 첫 번째 μ—°κ΅¬λŠ” 보쑰 감지 μž‘μ—…μ΄ μžˆλŠ” 쀀지도 μžλ™ 인코더λ₯Ό 톡해 건전성 평면을 μ œμ•ˆν•˜μ˜€λ‹€. μ œμ•ˆλœ 방법은 λ³€μ••κΈ° μ—΄ν•˜ νŠΉμ„±μ„ μ‹œκ°ν™” ν•  수 μžˆλ‹€. λ˜ν•œ, 쀀지도 접근법을 ν™œμš©ν•˜κΈ° λ•Œλ¬Έμ— λ°©λŒ€ν•œ λΉ„ν‘œμ§€λ°μ΄ν„° 그리고 μ†Œμˆ˜μ˜ ν‘œμ§€λ°μ΄ν„°λ§ŒμœΌλ‘œ κ΅¬ν˜„λ  수 μžˆλ‹€. μ œμ•ˆλ°©λ²•μ€ μ£Όλ³€μ••κΈ° 건전성을 건전성 평면과 ν•¨κ»˜ μ‹œκ°ν™”ν•˜κ³ , 맀우 적은 μ†Œμˆ˜μ˜ λ ˆμ΄λΈ” λ°μ΄ν„°λ§ŒμœΌλ‘œ μ£Όλ³€μ••κΈ° κ³ μž₯을 μ§„λ‹¨ν•œλ‹€. 두 번째 μ—°κ΅¬λŠ” κ·œμΉ™ 기반 Duval 방법을 AI 기반 deep neural network (DNN)κ³Ό μœ΅ν•©(bridge)ν•˜λŠ” μƒˆλ‘œμš΄ ν”„λ ˆμž„μ›Œν¬λ₯Ό μ œμ•ˆν•˜μ˜€λ‹€. 이 방법은 룰기반의 Duval을 μ‚¬μš©ν•˜μ—¬ λΉ„ν‘œμ§€λ°μ΄ν„°λ₯Ό μˆ˜λ„ λ ˆμ΄λΈ”λ§ν•œλ‹€ (pseudo-labeling). λ˜ν•œ, AI 기반 DNN은 μ •κ·œν™” 기술과 맀개 λ³€μˆ˜ 전이 ν•™μŠ΅μ„ μ μš©ν•˜μ—¬ λ…Έμ΄μ¦ˆκ°€ μžˆλŠ” pseudo-label 데이터λ₯Ό ν•™μŠ΅ν•˜λŠ”λ° μ‚¬μš©λœλ‹€. 개발된 κΈ°μˆ μ€ λ°©λŒ€ν•œμ–‘μ˜ λΉ„ν‘œμ§€λ°μ΄ν„°λ₯Ό 룰기반으둜 일차적으둜 μ§„λ‹¨ν•œ 결과와 μ†Œμˆ˜μ˜ μ‹€μ œ κ³ μž₯데이터와 ν•¨κ»˜ ν•™μŠ΅λ°μ΄ν„°λ‘œ ν›ˆλ ¨ν•˜μ˜€μ„ λ•Œ 기쑴의 진단 방법보닀 획기적인 ν–₯상을 κ°€λŠ₯μΌ€ ν•œλ‹€. 끝으둜, μ„Έ 번째 μ—°κ΅¬λŠ” κ³ μž₯ νƒ€μž…μ„ 진단할 뿐만 μ•„λ‹ˆλΌ 심각도 λ˜ν•œ μ§„λ‹¨ν•˜λŠ” κΈ°μˆ μ„ μ œμ•ˆν•˜μ˜€λ‹€. μ΄λ•Œ 두 μƒνƒœμ˜ λ ˆμ΄λΈ”λ§λœ κ³ μž₯ νƒ€μž…κ³Ό 심각도 μ‚¬μ΄μ—λŠ” λΆˆκ· μΌν•œ 데이터 λΆ„ν¬λ‘œ 이루어져 μžˆλ‹€. κ·Έ μ΄μœ λŠ” μ‹¬κ°λ„μ˜ 경우 λ ˆμ΄λΈ”λ§μ΄ 항상 λ˜μ–΄ μžˆμ§€λ§Œ κ³ μž₯ νƒ€μž…μ˜ κ²½μš°λŠ” μ‹€μ œ μ£Όλ³€μ••κΈ°λ‘œλΆ€ν„° κ³ μž₯ νƒ€μž… 데이터λ₯Ό μ–»κΈ°κ°€ 맀우 μ–΄λ ΅κΈ° λ•Œλ¬Έμ΄λ‹€. λ”°λΌμ„œ, λ³Έ μ—°κ΅¬μ—μ„œ μ„Έλ²ˆμ§Έλ‘œ κ°œλ°œν•œ κΈ°μˆ μ€ μ˜€λŠ˜λ‚  데이터 생성에 맀우 μš°μˆ˜ν•œ μ„±λŠ₯을 λ‹¬μ„±ν•˜κ³  μžˆλŠ” generative adversarial network (GAN)λ₯Ό 톡해 λΆˆκ· ν˜•ν•œ 두 μƒνƒœλ₯Ό 균일화 μž‘μ—…μ„ μˆ˜ν–‰ν•˜λŠ” λ™μ‹œμ— κ³ μž₯ λͺ¨λ“œμ™€ 심각도λ₯Ό μ§„λ‹¨ν•˜λŠ” λͺ¨λΈμ„ κ°œλ°œν•˜μ˜€λ‹€.Due to the rapid development and advancement of today’s 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 transformer’s 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 – with severity levels – 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 κ΅­λ¬Έ 초둝 127λ°•

    Condition Monitoring Methods for Large, Low-speed Bearings

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    In all industrial production plants, well-functioning machines and systems are required for sustained and safe operation. However, asset performance degrades over time and may lead to reduced effiency, poor product quality, secondary damage to other assets or even complete failure and unplanned downtime of critical systems. Besides the potential safety hazards from machine failure, the economic consequences are large, particularly in offshore applications where repairs are difficult. This thesis focuses on large, low-speed rolling element bearings, concretized by the main swivel bearing of an offshore drilling machine. Surveys have shown that bearing failure in drilling machines is a major cause of rig downtime. Bearings have a finite lifetime, which can be estimated using formulas supplied by the bearing manufacturer. Premature failure may still occur as a result of irregularities in operating conditions and use, lubrication, mounting, contamination, or external environmental factors. On the contrary, a bearing may also exceed the expected lifetime. Compared to smaller bearings, historical failure data from large, low-speed machinery is rare. Due to the high cost of maintenance and repairs, the preferred maintenance arrangement is often condition based. Vibration measurements with accelerometers is the most common data acquisition technique. However, vibration based condition monitoring of large, low-speed bearings is challenging, due to non-stationary operating conditions, low kinetic energy and increased distance from fault to transducer. On the sensor side, this project has also investigated the usage of acoustic emission sensors for condition monitoring purposes. Roller end damage is identified as a failure mode of interest in tapered axial bearings. Early stage abrasive wear has been observed on bearings in drilling machines. The failure mode is currently only detectable upon visual inspection and potentially through wear debris in the bearing lubricant. In this thesis, multiple machine learning algorithms are developed and applied to handle the challenges of fault detection in large, low-speed bearings with little or no historical data and unknown fault signatures. The feasibility of transfer learning is demonstrated, as an approach to speed up implementation of automated fault detection systems when historical failure data is available. Variational autoencoders are proposed as a method for unsupervised dimensionality reduction and feature extraction, being useful for obtaining a health indicator with a statistical anomaly detection threshold. Data is collected from numerous experiments throughout the project. Most notably, a test was performed on a real offshore drilling machine with roller end wear in the bearing. To replicate this failure mode and aid development of condition monitoring methods, an axial bearing test rig has been designed and built as a part of the project. An overview of all experiments, methods and results are given in the thesis, with details covered in the appended papers.publishedVersio
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