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

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

A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults

Real-time acquisition of large amounts of machine operating data is now increasingly common due to recent advances in Industry 4.0 technologies. A key benefit to factory operators of this large scale data acquisition is in the ability to perform real-time condition monitoring and early-stage fault detection and diagnosis on industrial machinery—with the potential to reduce machine down-time and thus operating costs. The main contribution of this work is the development of an intelligent fault diagnosis method capable of operating on these real-time data streams to provide early detection of developing problems under variable operating conditions. We propose a novel dual-path recurrent neural network with a wide first kernel and deep convolutional neural network pathway (RNN-WDCNN) capable of operating on raw temporal signals such as vibration data to diagnose rolling element bearing faults in data acquired from electromechanical drive systems. RNN-WDCNN combines elements of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to capture distant dependencies in time series data and suppress high-frequency noise in the input signals. Experimental results on the benchmark Case Western Reserve University (CWRU) bearing fault dataset show RNN-WDCNN outperforms current state-of-the-art methods in both domain adaptation and noise rejection tasks

Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning.

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets

A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series

This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtaining data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoder-encoder three-sub-network is employed in GAN, based on Deep Convolution Generative Adversarial Networks (DCGAN) but without Tanh activation layer and only trained on normal samples. In order to verify the validity and feasibility of our approach, we test it on rolling bearing data from Case Western Reserve University and further verify it on data collected from our laboratory. The results show that our proposed approach can achieve excellent performance in detecting faulty by outputting much larger evaluation scores