A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions

In recent years, intelligent fault diagnosis technology with the deep learning algorithm has been widely used in the manufacturing industry for substituting time-consuming human analysis method to enhance the efficiency of fault diagnosis. The rolling bearing as the connection between the rotor and support is the crucial component in rotating equipment. However, the working condition of the rolling bearing is under changing with complex operation demand, which will significantly degrade the performance of the intelligent fault diagnosis method. In this paper, a new deep transfer model based on Wasserstein distance guided multi-adversarial networks (WDMAN) is proposed to address this problem. The WDMAN model exploits complex feature space structures to enable the transfer of different data distributions based on multiple domain critic networks. The essence of our method is learning the shared feature representation by minimizing the Wasserstein distance between the source domain and target domain distribution in an adversarial training way. The experiment results demonstrate that our model outperforms the state-of-the-art methods on rolling bearing fault diagnosis under different working conditions. The t-distributed stochastic neighbor embedding (t-SNE) technology is used to visualize the learned domain invariant feature and investigate the transferability behind the great performance of our proposed model

Novel deep cross-domain framework for fault diagnosis or rotary machinery in prognostics and health management

Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This doctoral research provides a systematic review of state-of-the-art deep learning-based PHM frameworks, an empirical analysis on bearing fault diagnosis benchmarks, and a novel multi-source domain adaptation framework. It emphasizes the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. Besides, the limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. The empirical study of the benchmarks highlights the evaluation results of the existing models on bearing fault diagnosis benchmark datasets in terms of various performance metrics such as accuracy and training time. The result of the study is very important for comparing or testing new models. A novel multi-source domain adaptation framework for fault diagnosis of rotary machinery is also proposed, which aligns the domains in both feature-level and task-level. The proposed framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. Further, the proposed model is modified with a novel conditional weighting mechanism that aligns the class-conditional probability of the domains and reduces the effect of irrelevant source domain which is a critical issue in multi-source domain adaptation algorithms. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models

Exploiting gan as an oversampling method for imbalanced data augmentation with application to the fault diagnosis of an industrial robot

O diagnóstico inteligente de falhas baseado em aprendizagem máquina geralmente requer um conjunto de dados balanceados para produzir um desempenho aceitável. No entanto, a obtenção de dados quando o equipamento industrial funciona com falhas é uma tarefa desafiante, resultando frequentemente num desequilíbrio entre dados obtidos em condições nominais e com falhas. As técnicas de aumento de dados são das abordagens mais promissoras para mitigar este problema. Redes adversárias generativas (GAN) são um tipo de modelo generativo que consiste de um módulo gerador e de um discriminador. Por meio de aprendizagem adversária entre estes módulos, o gerador otimizado pode produzir padrões sintéticos que podem ser usados para amumento de dados. Investigamos se asGANpodem ser usadas como uma ferramenta de sobre amostra- -gem para compensar um conjunto de dados desequilibrado em uma tarefa de diagnóstico de falhas num manipulador robótico industrial. Realizaram-se uma série de experiências para validar a viabilidade desta abordagem. A abordagem é comparada com seis cenários, incluindo o método clássico de sobre amostragem SMOTE. Os resultados mostram que a GAN supera todos os cenários comparados. Para mitigar dois problemas reconhecidos no treino das GAN, ou seja, instabilidade de treino e colapso de modo, é proposto o seguinte. Propomos uma generalização da GAN de erro quadrado médio (MSE GAN) da Wasserstein GAN com penalidade de gradiente (WGAN-GP), referida como VGAN (GAN baseado numa matriz V) para mitigar a instabilidade de treino. Além disso, propomos um novo critério para rastrear o modelo mais adequado durante o treino. Experiências com o MNIST e no conjunto de dados do manipulador robótico industrial mostram que o VGAN proposto supera outros modelos competitivos. A rede adversária generativa com consistência de ciclo (CycleGAN) visa lidar com o colapso de modo, uma condição em que o gerador produz pouca ou nenhuma variabilidade. Investigamos a distância fatiada de Wasserstein (SWD) na CycleGAN. O SWD é avaliado tanto no CycleGAN incondicional quanto no CycleGAN condicional com e sem mecanismos de compressão e excitação. Mais uma vez, dois conjuntos de dados são avaliados, ou seja, o MNIST e o conjunto de dados do manipulador robótico industrial. Os resultados mostram que o SWD tem menor custo computacional e supera o CycleGAN convencional.Machine learning based intelligent fault diagnosis often requires a balanced data set for yielding an acceptable performance. However, obtaining faulty data from industrial equipment is challenging, often resulting in an imbalance between data acquired in normal conditions and data acquired in the presence of faults. Data augmentation techniques are among the most promising approaches to mitigate such issue. Generative adversarial networks (GAN) are a type of generative model consisting of a generator module and a discriminator. Through adversarial learning between these modules, the optimised generator can produce synthetic patterns that can be used for data augmentation. We investigate whether GAN can be used as an oversampling tool to compensate for an imbalanced data set in an industrial robot fault diagnosis task. A series of experiments are performed to validate the feasibility of this approach. The approach is compared with six scenarios, including the classical oversampling method (SMOTE). Results show that GAN outperforms all the compared scenarios. To mitigate two recognised issues in GAN training, i.e., instability and mode collapse, the following is proposed. We proposed a generalization of both mean sqaure error (MSE GAN) and Wasserstein GAN with gradient penalty (WGAN-GP), referred to as VGAN (the V-matrix based GAN) to mitigate training instability. Also, a novel criterion is proposed to keep track of the most suitable model during training. Experiments on both the MNIST and the industrial robot data set show that the proposed VGAN outperforms other competitive models. Cycle consistency generative adversarial network (CycleGAN) is aiming at dealing with mode collapse, a condition where the generator yields little to none variability. We investigate the sliced Wasserstein distance (SWD) for CycleGAN. SWD is evaluated in both the unconditional CycleGAN and the conditional CycleGAN with and without squeeze-and-excitation mechanisms. Again, two data sets are evaluated, i.e., the MNIST and the industrial robot data set. Results show that SWD has less computational cost and outperforms conventional CycleGAN

Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space

This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions. We evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU, where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in classification performance. Finally, we show that interpolations in the Wasserstein hull of learned atoms provide data that can generalize to the target domain.Comment: 13 pages,8 figures,Accepted as a conference paper at the 26th European Conference on Artificial Intelligenc

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Diagnosis of Bearing Damage in Mechanical Equipment Combining Fuzzy Logic Variable Phase Layered Algorithm

The paper aims at the problem that the bearing of mechanical equipment affects the safe, stable and efficient operation of mechanical equipment. In this paper, a fuzzy logic variable phase layered algorithm (flvpla) is proposed. The dimension reduction is realized by calculating the vibration signal. The vibration signal is effectively used to diagnose bearing fault, and the signal value is reduced to conduction fault classification. Finally, the experimental results show that the dimension reduction effect based on flvpla is better than that based on principal component analysis (PCA) algorithm and LTSA. The fault recognition rate of ba-svm is significantly higher than that of genetic algorithm optimized support vector machine (GA-SVM) and particle swarm optimization support vector machine (PSO-SVM). Therefore, the combination of flvpla and ba-svm can obtain higher recognition accuracy

딥러닝 기반 고장 진단을 위한 정보 활용 극대화 기법 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 윤병동.기계 시스템의 예기치 않은 고장은 많은 산업 분야에서 막대한 사회적, 경제적 손실을 야기할 수 있다. 갑작스런 고장을 감지하고 예방하여 기계 시스템의 신뢰성을 높이기 위해 데이터 기반 고장 진단 기술을 개발하기 위한 연구가 활발하게 이루어지고 있다. 고장 진단 기술의 목표는 대상 기계 시스템의 고장 발생을 가능한 빨리 감지하고 진단하는 것이다. 최근 합성곱 신경망 기법을 포함한 딥러닝 기반 고장 진단 기술은 자율적인 특성인자(feature) 학습이 가능하고 높은 진단 성능을 얻을 수 있다는 장점이 있어 활발히 연구되고 있다. 그러나 딥러닝 기반의 고장 진단 기술을 개발함에 있어 해결해야 할 몇 가지 문제점들이 존재한다. 먼저, 신경망 구조를 깊게 쌓음으로써 풍부한 계층적 특성인자들을 배울 수 있고, 이를 통해 향상된 성능을 얻을 수 있다. 그러나 기울기(gradient) 정보 흐름의 비효율성과 과적합 문제로 인해 모델이 깊어질수록 학습이 어렵게 된다는 문제가 있다. 다음으로, 높은 성능의 고장 진단 모델을 학습하기 위해서는 충분한 양의 레이블 데이터(labeled data)가 확보돼야 한다. 그러나 실제 현장에서 운용되고 있는 기계 시스템의 경우, 충분한 양의 데이터와 레이블 정보를 얻는 것이 어려운 경우가 많다. 따라서 이러한 문제들을 해결하고 진단 성능을 향상시키기 위한 새로운 딥러닝 기반 고장 진단 기술의 개발이 필요하다. 본 박사학위논문에서는 딥러닝 기반 고장 진단 기술의 성능을 향상시키기 위한 세가지 정보 활용 극대화 기법에 대한 연구로 1) 딥러닝 아키텍처 내 기울기 정보 흐름을 향상시키기 위한 새로운 딥러닝 구조 연구, 2) 파라미터 전이 및 삼중항 손실을 기반으로 불충분한 데이터 및 노이즈 조건 하 강건하고 차별적인 특성인자 학습에 대한 연구, 3) 다른 도메인으로부터 레이블 정보를 전이시켜 사용하는 도메인 적응 기반 고장 진단 기법 연구를 제안한다. 첫 번째 연구에서는 딥러닝 모델 내 기울기 정보 흐름을 개선하기 위한 향상된 합성곱 신경망 기반 구조를 제안한다. 본 연구에서는 다양한 계층의 아웃풋(feature map)을 직접 연결함으로써 향상된 정보 흐름을 얻을 수 있으며, 그 결과 진단 모델을 효율적으로 학습하는 것이 가능하다. 또한 차원 축소 모듈을 통해 학습 파라미터 수를 크게 줄임으로써 학습 효율성을 높일 수 있다. 두 번째 연구에서는 파라미터 전이 및 메트릭 학습 기반 고장 진단 기법을 제안한다. 본 연구는 데이터가 불충분하고 노이즈가 많은 조건 하에서도 높은 고장 진단 성능을 얻기 위해 강건하고 차별적인 특성인자 학습을 가능하게 한다. 먼저, 풍부한 소스 도메인 데이터를 사용해 훈련된 사전학습모델을 타겟 도메인으로 전이해 사용함으로써 강건한 진단 방법을 개발할 수 있다. 또한, semi-hard 삼중항 손실 함수를 사용함으로써 각 상태 레이블에 따라 데이터가 더 잘 분리되도록 해주는 특성인자를 학습할 수 있다. 세 번째 연구에서는 레이블이 지정되지 않은(unlabeled) 대상 도메인에서의 고장 진단 성능을 높이기 위한 레이블 정보 전이 전략을 제안한다. 우리가 목표로 하는 대상 도메인에서의 고장 진단 방법을 개발하기 위해 다른 소스 도메인에서 얻은 레이블 정보가 전이되어 활용된다. 동시에 새롭게 고안한 의미론적 클러스터링 손실(semantic clustering loss)을 여러 특성인자 수준에 적용함으로써 차별적인 도메인 불변 기능을 학습한다. 결과적으로 도메인 불변 특성을 가지며 의미론적으로 잘 분류되는 특성인자를 효과적으로 학습할 수 있음을 증명하였다.Unexpected failures of mechanical systems can lead to substantial social and financial losses in many industries. In order to detect and prevent sudden failures and to enhance the reliability of mechanical systems, significant research efforts have been made to develop data-driven fault diagnosis techniques. The purpose of fault diagnosis techniques is to detect and identify the occurrence of abnormal behaviors in the target mechanical systems as early as possible. Recently, deep learning (DL) based fault diagnosis approaches, including the convolutional neural network (CNN) method, have shown remarkable fault diagnosis performance, thanks to their autonomous feature learning ability. Still, there are several issues that remain to be solved in the development of robust and industry-applicable deep learning-based fault diagnosis techniques. First, by stacking the neural network architectures deeper, enriched hierarchical features can be learned, and therefore, improved performance can be achieved. However, due to inefficiency in the gradient information flow and overfitting problems, deeper models cannot be trained comprehensively. Next, to develop a fault diagnosis model with high performance, it is necessary to obtain sufficient labeled data. However, for mechanical systems that operate in real-world environments, it is not easy to obtain sufficient data and label information. Consequently, novel methods that address these issues should be developed to improve the performance of deep learning based fault diagnosis techniques. This dissertation research investigated three research thrusts aimed toward maximizing the use of information to improve the performance of deep learning based fault diagnosis techniques, specifically: 1) study of the deep learning structure to enhance the gradient information flow within the architecture, 2) study of a robust and discriminative feature learning method under insufficient and noisy data conditions based on parameter transfer and triplet loss, and 3) investigation of a domain adaptation based fault diagnosis method that propagates the label information across different domains. The first research thrust suggests an advanced CNN-based architecture to improve the gradient information flow within the deep learning model. By directly connecting the feature maps of different layers, the diagnosis model can be trained efficiently thanks to enhanced information flow. In addition, the dimension reduction module also can increase the training efficiency by significantly reducing the number of trainable parameters. The second research thrust suggests a parameter transfer and metric learning based fault diagnosis method. The proposed approach facilitates robust and discriminative feature learning to enhance fault diagnosis performance under insufficient and noisy data conditions. The pre-trained model trained using abundant source domain data is transferred and used to develop a robust fault diagnosis method. Moreover, a semi-hard triplet loss function is adopted to learn the features with high separability, according to the class labels. Finally, the last research thrust proposes a label information propagation strategy to increase the fault diagnosis performance in the unlabeled target domain. The label information obtained from the source domain is transferred and utilized for developing fault diagnosis methods in the target domain. Simultaneously, the newly devised semantic clustering loss is applied at multiple feature levels to learn discriminative, domain-invariant features. As a result, features that are not only semantically well-clustered but also domain-invariant can be effectively learned.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 6 Chapter 2 Technical Background and Literature Review 8 2.1 Fault Diagnosis Techniques for Mechanical Systems 8 2.1.1 Fault Diagnosis Techniques 10 2.1.2 Deep Learning Based Fault Diagnosis Techniques 15 2.2 Transfer Learning 22 2.3 Metric Learning 28 2.4 Summary and Discussion 30 Chapter 3 Direct Connection Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis 31 3.1 Directly Connected Convolutional Module 33 3.2 Dimension Reduction Module 34 3.3 Input Vibration Image Generation 36 3.4 DC-CNN-Based Fault Diagnosis Method 40 3.5 Experimental Studies and Results 45 3.5.1 Experiment and Data Description 45 3.5.2 Compared Methods 48 3.5.3 Diagnosis Performance Results 51 3.5.4 The Number of Trainable Parameters 56 3.5.5 Visualization of the Learned Features 58 3.5.6 Robustness of Diagnosis Performance 62 3.6 Summary and Discussion 67 Chapter 4 Robust and Discriminative Feature Learning for Fault Diagnosis Under Insufficient and Noisy Data Conditions 68 4.1 Parameter transfer learning 70 4.2 Robust Feature Learning Based on the Pre-trained model 72 4.3 Discriminative Feature Learning Based on the Triplet loss 77 4.4 Robust and Discriminative Feature Learning for Fault Diagnosis 80 4.5 Experimental Studies and Results 84 4.5.1 Experiment and Data Description 84 4.5.2 Compared Methods 85 4.5.3 Experimental Results Under Insufficient Data Conditions 86 4.5.4 Experimental Results Under Noisy Data Conditions 92 4.6 Summary and Discussion 95 Chapter 5 A Domain Adaptation with Semantic Clustering (DASC) Method for Fault Diagnosis 96 5.1 Unsupervised Domain Adaptation 101 5.2 CNN-based Diagnosis Model 104 5.3 Learning of Domain-invariant Features 105 5.4 Domain Adaptation with Semantic Clustering 107 5.5 Proposed DASC-based Fault Diagnosis Method 109 5.6 Experimental Studies and Results 114 5.6.1 Experiment and Data Description 114 5.6.2 Compared Methods 117 5.6.3 Scenario I: Different Operating Conditions 118 5.6.4 Scenario II: Different Rotating Machinery 125 5.6.5 Analysis and Discussion 131 5.7 Summary and Discussion 140 Chapter 6 Conclusion 141 6.1 Contributions and Significance 141 6.2 Suggestions for Future Research 143 References 146 국문 초록 154박

Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning

Data-driven prognostic and health management technologies are instrumental in accurately monitoring the health of mechanical systems. However, the availability of few-shot source data under varying operating conditions limits their ability to predict health. Also, the global feature extraction process is susceptible to temporal semantic loss, resulting in reduced generalization of extracted degradation features. To address these challenges, a transferable autoregressive recurrent adaptation method is proposed for bearing health prognosis. In the enhancement of few-shot data, a novel sample generation module with attribute-assisted learning, combined with adversarial generation, is introduced to mine data that better matches the source sample distribution. Additionally, a deep autoregressive recurrent model is designed, incorporating a statistical mode to consider the degradation processes more comprehensively. To complement the semantic loss, a semantic attention module is developed, embedded into the basic model of meta learning. To validate the effectiveness of this approach, extensive bearing prognostics are conducted across six tasks. The results demonstrate the clear advantages of this proposed method in bearing prognosis, especially when dealing with limited bearing data

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries