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

Support Vector Machine with Wavelet Decomposition Method for Fault Diagnosis of Tapered Roller Bearings by Modelling Manufacturing Defects

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구름요소 베어링 진단을 위한 딥러닝 기반 스폴 크기 분포 추정 연구

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2023. 2. 윤병동.When a rolling element bearing (REB) fails, the most common reason is the spall caused by rolling contact fatigue. In previous studies, when a ball passes through a spall, a step response with a low-frequency appears due to the effect of entering to the spall and an impulse response with a high-frequency appears when exiting the spall in the acceleration signal. Since the entry event signal is relatively weaker than the exit event signal and noise, research to date have attempted to estimate the location of the entry event using various signal processing technic such as noise reduction and strengthening the entry event features. However, in signal processing, manual parameter selection for finding the characteristics of entry event varies on bearing geometry and operating condition and since the parameter selection is empirical, the accuracy may differ accordingly. In addition, the spall size reflected in the signal also has uncertainty due to the geometry of the real spall and the uncertainty of rotation due to random slip. To overcome this difficulty, a deep learning-based approach was proposed in this study. The proposed architecture learned through analytic simulation signals which was generated by similar geometry and operating conditions to test data, the spall size was estimated without manual parameter selection. By obtaining the mean and variance from the estimated values obtained from the models trained with several kernels and strides, the spall size distribution was obtained. The proposed method was validated through experimental data. Through the performance analysis results, the proposed method was effective.구름 접촉 피로로 인한 스폴은 구름 요소 베어링 파손의 가장 일반적인 원인이며 스폴 크기 추정은 심각도를 추정하는 좋은 방법이 될 수 있다. 기존 연구에서는 구름 요소가 스폴 영역을 지나가는 과정에서, 진입할 때 저주파 단계 응답이 나타나고, 이탈할 때 고주파의 충격 응답이 나타난다고 알려져 있다. 진입이벤트 신호는 이탈이벤트 신호 및 노이즈에 비해 상대적으로 약하기 때문에 지금까지의 연구에서는 노이즈 감소, 진입이벤트 특성인자 강화 등의 다양한 신호처리 기술을 이용하여 진입이벤트의 시간적 위치를 추정하고자 하였다. 그러나 신호처리에서 진입이벤트의 특성을 찾기 위한 매개변수 선택은 베어링 형상이나 작동 조건 등에 따라 다르며, 선택이 경험적이므로 정확도가 경우에 따라 다를 수 있다. 또한 신호에 반영된 스폴의 크기도 실제 스폴의 일정하지 않은 모양에 의한 불확실성과 베어링 구름요소의 임의 미끄러짐으로 인한 회전의 불확실성을 가지고 있다. 이러한 어려움을 극복하기 위해 본 연구에서는 딥러닝 기반 접근 방식을 제안한다. 테스트 데이터와 유사한 형상 및 작동 조건인 해석적 시뮬레이션 신호를 통해 학습된 제안모델은 매개변수의 수동적 선택 없이 스폴의 크기를 추정한다. 여러 커널과 스트라이드가 선택되어 만들어진 여러 훈련모델에서 얻은 추정값을 통해 평균과 분산을 구하여 파편 크기 분포를 추정한다. 제안된 모델은 고장을 인가한 베어링을 통해 얻어진 실험 데이터로 검증한다. 성능 분석 결과는 제안된 접근 방식이 효과적임을 나타낸다.Chapter 1. Introduction 1 1.1 Introduction 1 1.2 Dissertation Layout 4 Chapter 2. Research Background 5 2.1 Spall Size Estimation Through the Time Interval 5 Chapter 3. Spall size distribution estimation for REB 7 3.1 Transformation of Input Signal 9 3.2 Signal Generation for Training 11 3.3 Denoising Autoencoder (DAE) 12 3.4 Spall Size Estimation Through the Time Interval 14 3.5 Spall Size Ensemble 17 Chapter 4. Experimental Validation 18 4.1 Experimental Setting 18 4.2 Training Signal Generation 20 4.3 Result 25 Chapter 5. Conclusions 32 Bibliography 33 국문 초록 37석

Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbor and support vector machine

Vibration technique in a machine condition monitoring provides useful reliable information, bringing significant cost benefits to industry. By comparing the signals of a machine running in normal and faulty conditions, detection of defected journal bearings is possible. This paper presents fault diagnosis of a journal bearing based on vibration analysis using three classifiers: Fisher Linear Discriminant (FLD), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The frequency-domain vibration signals of an internal combustion engine with intact and defective main journal bearings were obtained. 30 features were extracted by using statistical and vibration parameters. These features were used as inputs to the classifiers. Two different solution methods - variable K value and RBF kernel width (σ) were applied for FLD, KNN and SVM, respectively, in order to achieve the best accuracy. Finally, performance of the three classifiers was calculated in journal bearing fault diagnosis. The results demonstrated that the performance of SVM was significantly better in comparison to FLD and KNN. Also the results confirmed the potential of this procedure in fault diagnosis of journal bearings

An SVM-Based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels

Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis

A novel bearing multi-fault diagnosis approach based on weighted permutation entropy and an improved SVM ensemble classifier

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis