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

Domain Adaptive Transfer Learning for Fault Diagnosis

Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new machines in the same fleet. Although data-driven methods have shown great potential in fault diagnosis applications, their ability to generalize on new machines and new working conditions are limited because of their tendency to overfit to the training set in reality. One promising solution to this problem is to use domain adaptation techniques. It aims to improve model performance on the target new machine. Inspired by its successful implementation in computer vision, we introduced Domain-Adversarial Neural Networks (DANN) to our context, along with two other popular methods existing in previous fault diagnosis research. We then carefully justify the applicability of these methods in realistic fault diagnosis settings, and offer a unified experimental protocol for a fair comparison between domain adaptation methods for fault diagnosis problems.Comment: Presented at 2019 Prognostics and System Health Management Conference (PHM 2019) in Paris, Franc

Deep transfer learning for machine diagnosis: From sound and music recognition to bearing fault detection

Today’s deep learning strategies require ever‐increasing computational efforts and demand for very large amounts of labelled data. Providing such expensive resources for machine diagnosis is highly challenging. Transfer learning recently emerged as a valuable approach to address these issues. Thus, the knowledge learned by deep architectures in different scenarios can be reused for the purpose of machine diagnosis, minimizing data collecting efforts. Existing research provides evidence that networks pre‐trained for image recognition can classify machine vibrations in the time‐frequency domain by means of transfer learning. So far, however, there has been little discussion about the potentials included in networks pre‐trained for sound recognition, which are inherently suited for time‐frequency tasks. This work argues that deep architectures trained for music recognition and sound detection can perform machine diagnosis. The YAMNet convolutional network was designed to serve extremely efficient mobile applications for sound detection, and it was originally trained on millions of data extracted from YouTube clips. That framework is employed to detect bearing faults for the CWRU dataset. It is shown that transferring knowledge from sound and music recognition to bearing fault detection is successful. The maximum accuracy is achieved using a few hundred data for fine‐tuning the fault diagnosis model

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

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

With the continuous improvement of product quality, production efficiency, and complexity, higher requirements are put forward for the reliability and stability of equipment, and the difficulty of real-time diagnosis of faults and functional failures is also increasing. The traditional fault diagnosis methods based on signal processing and Convolutional neural network cannot meet the requirements of on-site online real-time fault diagnosis of equipment. One is that the vibration signals on the industrial site are superimposed on each other, nonlinear and unstable and traditional feature extraction methods take a long time, resulting in unstable extraction results. Second, massive data and fault diagnosis algorithms need rich computing and storage resources. The traditional Convolutional neural network method conflicts with the real-time response requirements of fault diagnosis. At the same time, different models of fault diagnosis models have poor generalization ability, and the diagnostic accuracy is not high or even impossible to diagnose. To solve the above problems, this paper proposes a fault diagnosis method based on industrial Internet platform, which is equipment cloud edge collaboration + adaptive countermeasure network Transfer learning. On the edge side, the vibration signals collected from key components of the model are processed using empirical mode decomposition (EEMD) to solve the problem of signal nonlinearity and stationarity. In the cloud, EEMD signals of different models are decomposed into source domain and target domain for confrontation training, which is used as the input of the improved domain adversarial network model DANN (Domain Adversarial Neural Networks), so as to improve the accuracy of fault diagnosis of different models by using cloud computing power and the improved adversarial network Transfer learning algorithm. Through the analysis of experimental data, this paper verifies that the model after the confrontation network Transfer learning is more accurate than the traditional fault diagnosis method. Through the coordination of computing resources and real-time requirements, real-time diagnosis of cloud side collaborative bearing fault is realized