Mechanisms of cage noise generation in machine tool bearings

Cage instability in ball bearings can lead to torque fluctuations and significant noise. In machine tool spindles, which require high rotational precision, outer ring-guided cages are often preferred over common ball-guided cages. While outer ring-guided cages suppress instability modes caused by sliding friction between the cage and balls, increased interaction between the cage and outer ring can introduce other instability modes, leading to noise. Despite the critical implications of these findings, prior research into this specific type of cage instability, incorporating both experimental and analytical perspectives, remains limited. Therefore, in this study, we utilized a high-speed camera system to conduct visualization tests on cage behavior in grease-lubricated angular contact ball bearings used in machine tools. Through detailed image-processing of the results, we identified specific behaviors associated with cage noise. To facilitate the optimal design of the cage to stabilize these behaviors, we developed a dynamic analysis model focusing on the friction between the cage and the outer ring under grease lubrication, considering fluid pressure effects. The validity of this model was confirmed through experiments at various rotational speeds. This analytical model enabled us to elucidate the underlying mechanisms driving cage instability. The insights gained from this research are expected to significantly enhance the fundamental understanding of cage design principles aimed at eliminating cage noise

Design Guide for Bearings Used in Cryogenic Turbopumps and Test Rigs

Cryogenic bearings are a unique and specialized area of the overall group of bearings that are used every day in industrial and aerospace applications. Cryogenic bearings operate in a unique environment that is not experienced by most bearing applications. The high speeds of turbomachinery, flow of cryogenic coolants, use of nonstandard materials, and lack of lubrication place unique demands on cryogenic bearings that must be met for the safety and success of the mission. To meet the goals of safety and success, requirements are put on the designer, manufacturer, and user that are not normally applied to off-the-shelf bearings. The designer has to have knowledge of the operating conditions, rotational speeds, loads, stresses, installation methods, inspection criteria, dimensional requirements, and design and analytical tools. The manufacturer needs to be aware of the materials used for cryogenic bearings, special heat treatments required, cleanliness of the processes, and inspection techniques to ensure a good product. The user needs to be aware of the safe handling practices to eliminate corrosion and debris, correct installation and removal procedures, pre- and post-test inspections, and the documentation that follow the bearings. This guide is based on the experiences of engineers at NASA Marshall Space Flight Center (MSFC) that have been involved in bearing research and testing along with specific bearing references that have been written. It is not meant to be a bearing design textbook for cryogenic bearing applications. These are available from many authors. Its purpose is to help the designer, manufacturer, or user in the application of cryogenic bearings to better understand the requirements placed on these bearings

Acoustic emission analysis for bearing condition monitoring

Acoustic emission (AE) was originally developed for non-destructive testing of static structures, however, over the years its application has been extended to health monitoring of rotating machines and bearings. It offers the advantage of earlier defect detection in comparison to vibration analysis. Current methodologies of applying AE for bearing diagnosis are reviewed. The investigation reported in this paper was centered on the application of standard acoustic emissions (AE) characteristic parameters on a rotational speed. An experimental test-rig was designed to allow seeded defects on the inner race, corrode and contaminated defect. It is concluded that irrespective of the rotational speed and high levels of background noise, simple AE parameters such as amplitude and AE counts provided an indications of bearing defect. In addition to validating already established AE techniques, this investigation focuses on establishing an appropriate threshold level for AE counts

Rolling-contact bearing reference summary

Design and performance of rolling contact bearing

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

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology

Advances in Bearing Lubrication and Thermal Sciences

This reprint focuses on the hot issue of bearing lubrication and thermal analysis, and brings together many cutting-edge studies, such as bearing multi-body dynamics, bearing tribology, new lubrication and heat dissipation structures, bearing self-lubricating materials, thermal analysis of bearing assembly process, bearing service state prediction, etc. The purpose of this reprint is to explore recent developments in bearing thermal mechanisms and lubrication technology, as well as the impact of bearing operating parameters on their lubrication performance and thermal behavior

Bearing Failure Prediction

Tato bakalářská práce zkoumá použití matematického modelu rozhodovacího stromu pro předpověď selhání ložisek v papírenských strojích pomocí analýzy amplitudy vibrací. Hlavní přínosy této práce zahrnují vývoj metodiky pro předpověď selhání ložisek s cílem zlepšit přesnost a efektivitu prediktivní údržby. To zahrnuje sběr a předzpracování dat, následně implementaci a testování modelu rozhodovacího stromu. Práce také zkoumá praktičnost nasazení takového modelu v průmyslovém prostředí, se zaměřením na ekonomické přínosy vyplývající z úspory času a zlepšení plánování údržby. Přestože disertace poskytuje cenné poznatky o předpovědi selhání ložisek, uznává také, že je třeba udělat více práce. Návrhy pro budoucí výzkum zahrnují zdokonalení procesu sběru dat, úpravu citlivosti modelu pro zpracování v reálném čase a zkoumání implementace prediktoru selhání ložisek na základě frekvence.This Bachelor's thesis examines the application of a mathematical decision tree model for predicting bearing failures in paper machines, using amplitude vibration analysis. The principal contributions of this thesis include the development of a methodology for predicting bearing failures, with a view to improving the accuracy and efficiency of predictive maintenance. This involves the collection and preprocessing of data, followed by the implementation and testing of a decision tree model. The thesis also explores the practicality of deploying such a model in an industrial setting, focusing on the economic benefits stemming from time savings and enhanced maintenance planning. While the dissertation provides valuable insights into bearing failure prediction, it also acknowledges that more work needs to be done. Suggestions for future research include refining the data collection process, adjusting model sensitivity for real-time processing, and exploring the implementation of a frequency failure bearing predicto