The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting

In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test

A robust fault detection method of rolling bearings using modulation signal bispectrum analysis

Envelope analysis is a widely used method for bearing fault detection. To obtain high detection accuracy, it is critical to select an optimal narrowband for envelope demodulation. Fast Kurtogram is an effective method for optimal narrowband selection. However, fast Kurtogram is not sufficiently robust because it is very sensitive to random noise and large aperiodic impulses which normally exist in practical application. To achieve the purpose of denoising and frequency band optimization, this paper proposes a new fault detector based on modulation signal bispectrum analysis (MSB) for bearing fault detection. As MSB results highlight the modulation effects by suppressing stationary random noise and discrete aperiodic impulses, the detector developed using high magnitudes of MSB can provide optimal frequency bands for fault detection straightforward. Performance evaluation results using both simulated data and experimental data show that the proposed method produces more effective and robust detection results for different types of bearing faults, compared with optimal envelope analysis using fast Kurtogram

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

진동 생성 메커니즘을 고려한 초기 결함 단계의 베어링 진단 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 윤병동.The failure of rolling element bearings is a common fault in rotating machines. These failures can have catastrophic consequences, including fatal injuries and significant financial losses. To mitigate these risks, researchers have explored various ways to detect and prevent bearing failures as early as possible. One promising approach is the use of condition monitoring data; in this approach, vibration data has been found to be particularly effective for identifying and preventing potential failures. However, the use of vibration signals to diagnose bearings at the incipient fault stage is a challenging task, in part due to the gap between the controlled conditions under which research data is often generated and the actual field conditions in which these bearings operate. In particular, fault-related signals are weak and nonstationary; further, they are usually obscured by noise that arises from environmental factors. Additionally, these signals may be complicated or modulated, making them difficult to discern. To properly address these research issues, this dissertation aims at advancing two research thrusts focused on developing techniques for modeling and analyzing vibration signals based on physical phenomena. In Research Thrust 1, a quasi-periodic impulse train model with an impact force function is suggested to brtidge the gap between theory and reality. In this research, a pseudo second-order cyclostationary signal is modeled using the quasi-periodic impulse train model. In order to simulate the dynamic response of a system, considering the physical behaviors in bearings, the impact force function that reflects the change in contact stress is used. Finally, the proposed model is validated by performing signal processing on the synthesized signal, including simulation of the proposed model. The result confirm that an appropriate preprocessing process is essential to diagnose bearing failure at the incipient failure stage, further, that finding the frequency band that contains the failure information is essential for performance improvement. In Research Thrust 2, a new feature extraction method is proposed for bearing diagnosis using vibration signals, namely the linear power normalized cepstral coefficients (LPNCC). The proposed approach is designed to enhance the bearing signal, which is buried in noise that arises from environmental effects, and which contains mechanical phenomena. The proposed method consists of two sequentially executed steps: 1) extraction of the LPNCC and 2) demodulation analysis that is performed by examining the squared envelope spectra (SES). Combined, this approach is called LPNCC-SES. The performance of the proposed method is examined by applying it to both simulation data and experimental cases. The results show a high level of accuracy and robustness in the diagnostic capabilities of the method, making it suitable for use in maintenance and diagnostic routines.구름 베어링은 회전 기계 및 왕복동 기계의 핵심적인 요소부품으로 회전하거나 진동하는 구조를 지지하며 구성품 간의 하중을 전달한다. 따라서 구름 베어링의 고장은 시스템 전체의 고장으로 이어져 치명적인 인명 피해는 물론 막대한 재정적 손실을 초래할 수 있다. 이에 따라 조기에 베어링의 고장을 관측하고 진단하기 위해 상태관측 데이터를 활용한 많은 연구가 진행되어 왔으며 특히 진동신호를 활용한 진단이 널리 수행되었다. 베어링 초기 결함을 진단을 하는데 있어 어려움을 겪게 하는 이유로 환경 영향으로 인해 발생하는 잡음에 묻혀 있는 약한 결함 신호 및 베어링의 결함 관련 신호의 복잡한 변조를 들 수있다. 이러한 문제를 극복하기 위해 본 연구에서는 베어링 결함신호의 생성원리에에 기반한 신호 모델을 제안하였다. 베어링 신호는 본질적으로 비정상성을 띄며 또한 실제 현장에서 획득한 신호는 복잡하고 다양한 소스에서 발생하는 신호가 조합된다. 이론과 현실 사이의 격차를 해소하기 위해 해석적 신호 모델에 헤르츠 접촉 이론에 기반한 충격 메커니즘을 구현하였다. 시뮬레이션된 베어링 신호에 기어의 결정론적 신호, 회전축의 사인파 신호 및 가우시안 노이즈와 합성된 신호에 대한 전처리 분석을 통해 제안 모델의 타당성을 검증하였다. 이 후, 다양한 잡음 환경에서 여러 변조된 음성 신호를 효과적으로 판별하는 음성인식 방법을 기계시스템에 적용한 고장특징 추출 방법을 새로이 제안하여 캡스트럽에 기반한 특징인자를 추출하였다. 추출된 인자로부터 시간-주파수 영역에서 스펙트럼을 계산하여 효과적으로 베어링의 특성 주파수를 검출하였다. 제안된 방법의 검증을 위해 다양한 잡음 환경에서의 시뮬레이션 데이터와 실험데이터를 사용하였다. 또한 가속수명시험을 통한 데이터를 통하여 조기진단의 효과를 검증하였다.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 5 Chapter 2. Technical Background and Literature Review 6 2.1 Vibration Signals of Bearing Faults 6 2.1.1 Rolling Element Bearings 6 2.1.2 Failure of Rolling Element Bearings 7 2.1.3 Bearing Fault Signature and Its Frequencies 8 2.2 Vibration Techniques for Bearing Incipient Fault Diagnosis 10 2.2.1 Overview of Vibration Techniques for Bearings 10 2.2.2 Cepstrum-Based Fault Diagnosis Techniques 13 Chapter 3. Quasi Periodic Impulse Train Model with Impact Force Function 20 3.1 Vibration Modelling of Bearing Fault 21 3.1.1 General Mathematical Model 21 3.1.2 Quasi-periodic Model with Cyclostationary 22 3.1.3 Excitation Force Function in Dynamic Models 23 3.2 Quasi Period Impulse Model with Impact Function 26 3.2.1 Overall Process of Proposed Model 26 3.2.2 Modeling the Excitation Force 27 3.3 Numerical Results and Discussion 32 3.3.1 Necessity of Choosing an Appropriate Preprocessing Method 34 Chapter 4. Speech Recognition-Inspired Feature Engineering for Bearing Fault Diagnosis 48 4.1 Review of Power-Normalized Cepstral Coefficients (PNCC) 49 4.1.1 Basic Definition of Cepstrum 49 4.1.2 Characteristics of cepstrum in mechanical vibrations 50 4.1.3 Power-Normalized Cepstral Coefficients (PNCC) 52 4.2 Proposed Feature Extraction Method: Linear Power-Normalized Cepstral Coefficients (LPNCC) 55 4.3 Fault Diagnosis by Implementing LPNCC 57 4.3.1 Fault Diagnosis Method using LPNCC and Squared Envelope Spectrum (LPNCC-SES) 57 4.3.2 Effect of Linear Filter and Power-normalization 59 4.4 Experimental Application and Results 60 4.4.1 Case Study with Simulation Model 61 4.4.1.1. Simulation Data with White Gaussian Noise 61 4.4.1.2. Denoising Under Gaussian Noise 62 4.4.1.3. Reseults Under Non-gaussian Noise 66 4.4.2 Case Study with Experiment Data 67 4.4.2.1. Experimental Data: Case Western Reserve University Dataset 67 4.4.2.1.1. Compared Methods 67 4.4.2.1.2. Case 1: Impusive Noise 68 4.4.2.1.3. Case 2: Low Signal-to-noise Ratio (SNR) 69 4.4.2.1.4. Case 3: Multiple Defective Signals 71 4.4.2.2. Experimental Data: Naturally Degradation Data 72 Chapter 5. Conclusions 108 5.1 Summary of Dissertation 108 5.2 Contributions and Significance 110 5.3 Suggestions for Future Research 113 References 116 Abstract (Korean) 130박

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

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