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

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

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

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

비표지 고장 데이터와 유중가스분석데이터를 이용한 딥러닝기반 주변압기 고장진단 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 소재웅.오늘날 산업의 급속한 발전과 고도화로 인해 안전하고 신뢰할 수 있는 전력 계통에 대한 수요는 더욱 중요해지고 있다. 따라서 실제 산업 현장에서는 주변압기의 안전한 작동을 위해 상태를 정확하게 진단할 수 있는 prognostics and health management (PHM)와 같은 기술이 필요하다. 주변압기 진단을 위해 개발된 다양한 방법 중 인공지능(AI) 기반 접근법은 산업과 학계에서 많은 관심을 받고 있다. 더욱이 방대한 데이터와 함께 높은 성능을 달성하는 딥 러닝 기술은 주변압기 고장 진단의 학자들에게 높은 관심을 갖게 해줬다. 그 이유는 딥 러닝 기술이 시스템의 도메인 지식을 깊이 이해할 필요 없이 대량의 데이터만 주어진다면 복잡한 시스템이라도 사용자의 목적에 맞게 그 해답을 찾을 수 있기 때문에 딥 러닝에 대한 관심은 주변압기 고장 진단 분야에서 특히 두드러졌다. 그러나, 이러한 뛰어난 진단 성능은 아직 실제 주변압기 산업에서는 많은 관심을 얻고 있지는 못한 것으로 알려졌다. 그 이유는 산업현장의 비표지데이터와 소량의 고장데이터 때문에 우수한 딥러닝기반의 고장 진단 모델들을 개발하기 어렵다. 따라서 본 학위논문에서는 주변압기 산업에서 현재 대두되고 있는 세가지 이슈를 연구하였다. 1) 건전성 평면 시각화 이슈, 2) 데이터 부족 이슈, 3) 심각도 이슈 들을 극복하기 위한 딥 러닝 기반 고장 진단 연구를 진행하였다. 소개된 세가지 이슈들을 개선하기 위해 본 학위논문은 세 가지 연구를 제안하였다. 첫 번째 연구는 보조 감지 작업이 있는 준지도 자동 인코더를 통해 건전성 평면을 제안하였다. 제안된 방법은 변압기 열하 특성을 시각화 할 수 있다. 또한, 준지도 접근법을 활용하기 때문에 방대한 비표지데이터 그리고 소수의 표지데이터만으로 구현될 수 있다. 제안방법은 주변압기 건전성을 건전성 평면과 함께 시각화하고, 매우 적은 소수의 레이블 데이터만으로 주변압기 고장을 진단한다. 두 번째 연구는 규칙 기반 Duval 방법을 AI 기반 deep neural network (DNN)과 융합(bridge)하는 새로운 프레임워크를 제안하였다. 이 방법은 룰기반의 Duval을 사용하여 비표지데이터를 수도 레이블링한다 (pseudo-labeling). 또한, AI 기반 DNN은 정규화 기술과 매개 변수 전이 학습을 적용하여 노이즈가 있는 pseudo-label 데이터를 학습하는데 사용된다. 개발된 기술은 방대한양의 비표지데이터를 룰기반으로 일차적으로 진단한 결과와 소수의 실제 고장데이터와 함께 학습데이터로 훈련하였을 때 기존의 진단 방법보다 획기적인 향상을 가능케 한다. 끝으로, 세 번째 연구는 고장 타입을 진단할 뿐만 아니라 심각도 또한 진단하는 기술을 제안하였다. 이때 두 상태의 레이블링된 고장 타입과 심각도 사이에는 불균일한 데이터 분포로 이루어져 있다. 그 이유는 심각도의 경우 레이블링이 항상 되어 있지만 고장 타입의 경우는 실제 주변압기로부터 고장 타입 데이터를 얻기가 매우 어렵기 때문이다. 따라서, 본 연구에서 세번째로 개발한 기술은 오늘날 데이터 생성에 매우 우수한 성능을 달성하고 있는 generative adversarial network (GAN)를 통해 불균형한 두 상태를 균일화 작업을 수행하는 동시에 고장 모드와 심각도를 진단하는 모델을 개발하였다.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 국문 초록 127박

Condition Monitoring Methods for Large, Low-speed Bearings

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