Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio

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

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images

Although the orbit analysis (orbit shape and size) is commonly used to diagnose rotating machinery, the diagnosis heavily depends on the expert knowledge or experience due to the difficulties of extracting mathematical features for data-driven approaches. Therefore, in this paper, we propose an autonomous orbit pattern recognition algorithm using the deep learning method on shaft orbit shape images. In details, the convolutional neural network is implemented to construct weights between neurons and to generate the entire structure of the neural network. Then, the created network enables us to classify fault modes of rotating machinery via orbit images. Furthermore, we demonstrate the proposed framework through a rotating testbed

불충분한 고장 데이터에 대한 딥러닝 기반 회전 기계 진단기술 학습방법 연구

학위논문(박사)--서울대학교 대학원 :공과대학 기계항공공학부,2020. 2. 윤병동.Deep Learning is a promising approach for fault diagnosis in mechanical applications. Deep learning techniques are capable of processing lots of data in once, and modelling them into desired diagnostic model. In industrial fields, however, we can acquire tons of data but barely useful including fault or failure data because failure in industrial fields is usually unacceptable. To cope with this insufficient fault data problem to train diagnostic model for rotating machinery, this thesis proposes three research thrusts: 1) filter-envelope blocks in convolution neural networks (CNNs) to incorporate the preprocessing steps for vibration signal; frequency filtering and envelope extraction for more optimal solution and reduced efforts in building diagnostic model, 2) cepstrum editing based data augmentation (CEDA) for diagnostic dataset consist of vibration signals from rotating machinery, and 3) selective parameter freezing (SPF) for efficient parameter transfer in transfer learning. The first research thrust proposes noble types of functional blocks for neural networks in order to learn robust feature to the vibration data. Conventional neural networks including convolution neural network (CNN), is tend to learn biased features when the training data is acquired from small cases of conditions. This can leads to unfavorable performance to the different conditions or other similar equipment. Therefore this research propose two neural network blocks which can be incorporated to the conventional neural networks and minimize the preprocessing steps, filter block and envelope block. Each block is designed to learn frequency filter and envelope extraction function respectively, in order to induce the neural network to learn more robust and generalized features from limited vibration samples. The second thrust presents a new data augmentation technique specialized for diagnostic data of vibration signals. Many data augmentation techniques exist for image data with no consideration for properties of vibration data. Conventional techniques for data augmentation, such as flipping, rotating, or shearing are not proper for 1-d vibration data can harm the natural property of vibration signal. To augment vibration data without losing the properties of its physics, the proposed method generate new samples by editing the cepstrum which can be done by adjusting the cepstrum component of interest. By doing reverse transform to the edited cepstrum, the new samples is obtained and this results augmented dataset which leads to higher accuracy for the diagnostic model. The third research thrust suggests a new parameter repurposing method for parameter transfer, which is used for transfer learning. The proposed SPF selectively freezes transferred parameters from source network and re-train only unnecessary parameters for target domain to reduce overfitting and preserve useful source features when the target data is limited to train diagnostic model.딥러닝은 기계 응용 분야의 결함 진단을 위한 유망한 접근 방식이다. 딥러닝 기술은 많은 양의 데이터를 학습하여 진단 모델의 개발을 용이하게 한다. 그러나 산업 분야에서는 많은 양의 데이터를 얻을 수 없거나 얻을 수 있더라도 고장 데이터는 일반적으로 획득하기 매우 어렵기 때문에 딥러닝 방법의 사용은 쉽지 않다. 회전 기계의 진단을 위하여 딥러닝을 학습시킬 때 발생하는 고장 데이터 부족 문제에 대처하기 위해 이 논문은 3 가지 연구를 제안한다. 1) 향상된 진동 특징 학습을 위한 필터-엔벨롭 네트워크 구조 2) 진동데이터 생성을 위한 Cepstrum 기반 데이터 증량법3) 전이 학습에서 효율적인 파라미터 전이를 위한 선택적 파라미터 동결법. 첫 번째 연구는 진동 데이터에 대한 강건한 특징을 배우기 위해 신경망에 대한 새로운 형태의 네트워크 블록들을 제안한다. 합성곱 신경망을 포함하는 종래의 신경망은 학습 데이터가 작은 경우에 데이터로부터 편향된 특징을 배우는 경향이 있으며, 이는 다른 조건에서 작동하는 경우나 다른 시스템에 대해 적용되었을 때 낮은 진단 성능을 보인다. 따라서 본 연구는 기존의 신경망에 함께 사용될 수 있는 필터 블록 및 엔벨롭 블록을 제안한다. 각 블록은 주파수 필터와 엔벨롭 추출 기능을 네트워크 내에서 스스로 학습하여 신경망이 제한된 학습 진동데이터로부터 보다 강건하고 일반화 된 특징을 학습하도록 한다. 두 번째 연구는 진동 신호의 진단 데이터에 특화된 새로운 데이터 증량법을 제안한다. 뒤집기, 회전 또는 전단과 같은 데이터 확대를 위한 이미지 데이터를 위한 기존의 기술이 1 차원 진동 데이터에 적합하지 않으며, 진동 신호의 물리적 특성에 맞지 않는 신호를 생성할 수 있다. 물리적 특성을 잃지 않고 진동 데이터를 증량하기 위해 제안된 방법은 cepstrum의 주요성분을 추출하고 조정하여 역 cepstrum을 수행하는 방식으로 새로운 샘플을 생성한다. 제안된 방법을 통해 데이터를 생성하여 증량돤 데이터세트는 진단 모델 학습에 대해 성능향상을 가져온다. 세 번째 연구는 전이 학습에 사용되는 파라미터 전이를 위한 새로운 파라미터 재학습법을 제안한다. 제안된 선택적 파라미터 동결법은 소스 네트워크에서 전이된 파라미터를 선택적으로 동결하고 대상 도메인에 대해 불필요한 파라미터만 재학습하여 대상 데이터가 진단 모델에 재학습될 때의 과적합을 줄이고 소스 네트워크의 성능을 보존한다. 제안된 세 방법은 독립적으로 또는 동시에 진단모델에 사용되어 부족한 고장데이터로 인한 진단성능의 감소를 경감하거나 더 높은 성능을 이끌어낼 수 있다.Chapter 1 Introduction 13 1.1 Motivation 13 1.2 Research Scope and Overview 15 1.3 Structure of the Thesis 19 Chapter 2 Literature Review 20 2.1 Deep Neural Networks 20 2.2 Transfer Learning and Parameter Transfer 23 Chapter 3 Description of Testbed Data 26 3.1 Bearing Data I: Case Western Reserve University Data 26 3.2 Bearing Data II: Accelerated Life Test Test-bed 27 Chapter 4 Filter-Envelope Blocks in Neural Network for Robust Feature Learning 32 4.1 Preliminary Study of Problems In Use of CNN for Vibration Signals 34 4.1.1 Class Confusion Problem of CNN Model to Different Conditions 34 4.1.2 Benefits of Frequency Filtering and Envelope Extraction for Fault Diagnosis in Vibration Signals 37 4.2 Proposed Network Block 1: Filter Block 41 4.2.1 Spectral Feature Learning in Neural Network 42 4.2.2 FIR Band-pass Filter in Neural Network 45 4.2.3 Result and Discussion 48 4.3 Proposed Neural Block 2: Envelope Block 48 4.3.1 Max-Average Pooling Block for Envelope Extraction 51 4.3.2 Adaptive Average Pooling for Learnable Envelope Extractor 52 4.3.3 Result and Discussion 54 4.4 Filter-Envelope Network for Fault Diagnosis 56 4.4.1 Combinations of Filter-Envelope Blocks for the use of Rolling Element Bearing Fault Diagnosis 56 4.4.2 Summary and Discussion 58 Chapter 5 Cepstrum Editing Based Data Augmentation for Vibration Signals 59 5.1 Brief Review of Data Augmentation for Deep Learning 59 5.1.1 Image Augmentation to Enlarge Training Dataset 59 5.1.2 Data Augmentation for Vibration Signal 61 5.2 Cepstrum Editing based Data Augmentation 62 5.2.1 Cepstrum Editing as a Signal Preprocessing 62 5.2.2 Cepstrum Editing based Data Augmentation 64 5.3 Results and Discussion 65 5.3.1 Performance validation to rolling element bearing diagnosis 65 Chapter 6 Selective Parameter Freezing for Parameter Transfer with Small Dataset 71 6.1 Overall Procedure of Selective Parameter Freezing 72 6.2 Determination Sensitivity of Source Network Parameters 75 6.3 Case Study 1: Transfer to Different Fault Size 76 6.3.1 Performance by hyperparameter α 77 6.3.2 Effect of the number of training samples and network size 79 6.4 Case Study 2: Transfer from Artificial to Natural Fault 81 6.4.1 Diagnostic performance for proposed method 82 6.4.2 Visualization of frozen parameters by hyperparameter α 83 6.4.3 Visual inspection of feature space 85 6.5 Conclusion 87 Chapter 7 91 7.1 Contributions and Significance 91Docto

Bearing fault diagnosis method based on Hilbert envelope spectrum and deep belief network

When fault occurs in bearing, the frequency spectrum of vibration signal would change and it contains a considerable amount of fault information which can reflect the actual work condition and the fault type of bearing. Recently, the statistical features of the frequency spectrum have been widely used in bearing fault diagnosis. However, there are lots of statistical features with different sensitivity to fault identification. Selecting the most sensible statistical features for improving classification accuracy is often determined with experience, which will make great subjective influence on the fault diagnosis results. Deep belief network (DBN) is a deep neural network which can automatically find a latent hierarchical feature representation from the high dimension input data. In this study, a bearing fault diagnosis method based on Hilbert envelope spectrum and DBN is proposed. Firstly, the vibration signals under different test conditions are resampled. Secondly, the whole Hilbert envelope spectrum of the resampled signal is used directly as eigenvector to characterize the fault type of bearing. Finally, a DBN classifier model is established to recognize the fault type of bearing. DBN classifier model can be used as both an automatic feature extractor and a classifier for bearing fault diagnosis. Therefore, the process of fault diagnosis can be greatly simplified. The results of two different experiments demonstrate that the proposed method outperforms the competing methods and it can obtain a more excellent diagnostic performance