Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis

© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] With the expansion of the use of electrical drive sys- tems to more critical applications, the issue of reliability and fault mitigation and condition-based maintenance have consequently taken an increasing importance: it has become a crucial one that cannot be neglected or dealt with in an ad-hoc way. As a result research activity has increased in this area, and new methods are used, some based on a continuation and improvement of previous accomplishments, while others are applying theory and techniques in related areas. This Special Section of the IEEE Transactions on Industrial Informatics attracted a number of papers dealing with Advanced Signal and Image Processing Techniques for Electric Machine and Drives Fault Diagnosis and Prognosis. This editorial aims to put these contributions in context, and highlight the new ideas and directions therein.Antonino-Daviu, J.; Lee, SB.; Strangas, E. (2017). Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis. IEEE Transactions on Industrial Informatics. 13(3):1257-1260. doi:10.1109/TII.2017.2690464S1257126013

Novel Methods Based on Deep Learning Applied to Condition Monitoring in Smart Manufacturing Processes

The Industry 4.0 is the recent trend of automation and the rotating machinery takes a role of great relevance when it comes to meet the demands and challenges of smart manufacturing. Condition-based monitoring (CBM) schemes are the most prominent tool to cover the task of predictive diagnosis. With the current demand of the industry and the increasing complexity of the systems, it is vital to incorporate CBM methodologies that are capable of facing the variability and complexity of manufacturing processes. In recent years, various deep learning techniques have been applied successfully in different areas of research, such as image recognition, robotics, and the detection of abnormalities in clinical studies; some of these techniques have been approaching to the diagnosis of the condition in rotating machinery, promising great results in the Industry 4.0 era. In this chapter, some of the deep learning techniques that promise to make important advances in the field of intelligent fault diagnosis in industrial electromechanical systems will be addressed

Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions

Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniques are developed and tested under strictly controlled conditions, limiting their adaptability to the diverse and dynamic settings encountered in practical applications. This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults under various noise levels and time-varying rotational speeds. Additionally, we propose a novel Fisher-based spectral separability analysis (SSA) method to elucidate the effectiveness of the designed CNN model. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults. The experimental results show the superiority of our model over the current state-of-the-art approach in three folds: it achieves substantial accuracy gains of up to 15.8%, it is robust to noise with high performance across various signal-to-noise ratios, and it runs in real-time with processing durations five times less than acquisition. Additionally, by using the proposed SSA technique, we offer insights into the model's performance and underscore its effectiveness in tackling real-world challenges

물류자동화 시스템의 고장진단을 위한 특징 분석 및 군집 적응형 네트워크 연구

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022.2. 윤병동.This paper proposes a Feature-analytic, Fleet-adaptive Network (FAFAN) for fault diagnosis of automated material handling systems (AMHSs) in semiconductor fabs. Constructing a fault-diagnosis model for a fleet of Overhead Hoist Transports (OHTs), which are the central part of AMHSs in semiconductor fabs, is challenging since the torque signals from different OHT units diverge from each other; further, the signals from many units consist of both labeled data and unlabeled data. To effectively deal with this situation, the proposed method learns fault-discriminative and OHT unit-domain-invariant features by selectively using pre-processed, multi-channel torque signals. Next, the approach independently extracts features from each channel and automatically learns the channel weights to leverage them, considering domain generalizability and the presence of fault signatures. The proposed method consists of three main steps; 1) dividing the OHT dataset into a fully labeled source domain and a sparsely labeled target unit domain, 2) pre-processing front and rear torque signals into three-channel signals, and 3) extracting features to classify signals into normal, wheel fault, and gear fault states, while minimizing domain discrepancy through the use of semi-supervised domain adaptation. We demonstrate the effectiveness of the proposed method using data from 20 OHT units gathered from an actual industrial line, in numerous combinations of OHT unit domains, and different portions of target-domain-labeled data. The results of the validation verify that the proposed method is effective for fault diagnosis of a group of OHTs under insufficient label conditions and, further, that it provides physical evidence of the diagnosing conditions.본 논문은 반도체 공장의 물류자동화 시스템 (AMHS)의 고장 진단을 위한 특징 분석 및 군집 적응형 네트워크 (FAFAN)를 제안한다. 반도체 공장 AMHS의 핵심인 천장 반송 시스템 (OHT) 군집에 대한 고장 진단 모델을 구축하는 것은, 각 OHT 호기별로 토크 신호의 편차가 존재하기 때문에 어렵다. 또한, 많은 호기에서 취득되는 신호는 정상/고장 레이블이 있는 데이터와 레이블이 없는 데이터로 구성되어 있다. 이러한 상황에서 제안된 방법은, 전처리된 다채널 토크 신호를 활용하여 고장을 진단함과 동시에 OHT 호기 도메인에 대한 일반적인 특징을 학습한다. 특히, 전처리된 입력 채널에서 특징을 독립적으로 추출하고 도메인 일반화 가능성과 고장 진단의 정보량을 모델 학습 과정에 활용하기 위해 채널 가중치를 자동으로 학습한다. 제안된 방법은 1) 레이블이 있는 데이터로만 구성된 소스 도메인과, 레이블이 있는 데이터가 매우 적은 타겟 도메인으로 OHT 데이터세트를 나누는 단계, 2) 전면 및 후면 토크 신호를 3채널 신호로 전처리하는 단계, 그리고 3) 준지도 도메인 적응을 활용하여 OHT 호기 도메인 간의 신호 편차를 최소화함과 동시에 정상, 바퀴 결함 및 기어 결함 상태로 분류하는 특징을 추출하는 단계로 구성된다. 실제 산업 현장에서 수집된 20개의 OHT 호기 데이터에 대해, 많은 OHT 호기 도메인 조합 및 타겟 도메인 레이블 데이터의 다양한 비율 조합을 활용하여 제안된 방법의 효과를 입증한다. 검증 결과, 제안된 방법이 불충분한 레이블 데이터 조건에서 OHT 군집의 고장 진단에 효과적이며, 나아가 진단 결과의 물리적 근거를 제공함을 확인하였다.Abstract i List of Tables vi List of Figures vii Nomenclatures viii Chapter 1. Introduction 1 1.1 Research motivation 1 1.2 Research scope 3 1.3 Dissertation Layout 4 Chapter 2. Background 5 2.1 Overhead Hoist Transport (OHT) 5 2.2 Characteristics of the control torque signals of OHTs 6 Chapter 3. Proposed method 9 3.1 Configuration of the proposed FAFAN method 10 3.1.1 Pre-processing module 12 3.1.2 Feature extractor F: Channel-independent CNN 13 3.1.3 Feature extractor F: Channel-weighting block 13 3.1.4 Task module: Condition classifier C & Domain discriminator D 16 3.2 Model training procedures 16 3.2.1 Train F and C to classify the condition 16 3.2.2 Train D using to discriminate the OHT unit domain 17 3.2.3 Train F, C, and D to learn generalized feature representation for the source and target domains 18 Chapter 4. Experimental validation 20 4.1 Dataset description 21 4.2 Description of the comparison methods 23 4.2.1 Source only (S-only) 23 4.2.2 Source Target Labeled (STL) 23 4.2.3 Source Target Labeled CSA loss (STL-CSA) 23 4.2.4 Source Target Labeled CSA loss with Maximum Mean Discrepancy (STL-CSA-MMD) 24 4.3 Experimental settings 25 4.4 Results and discussion 28 4.4.1 Performance analysis 28 4.4.2 Input channel investigation 31 Chapter 5. Conclusion 34 5.1 Summary 34 5.2 Contribution 34 5.3 Future work 36 References 37 국문 초록 42석