Element Bearing(요소 베어링)란 무엇입니까?
Element Bearing 요소 베어링 - Rolling-element bearings (REB) can develop severe damage due to skidding (slipping) between the rolling elements and bearing races. [1] To address this problem spherical rolling-element bearings are used which have the capacity to encounter such misalignment till a certain limit. [2] Different strategies are commonly employed by researchers in order to decrease the computational effort associated with the finite-element analysis of rolling-element bearings. [3] This paper presents a data-driven prognostic framework for rolling-element bearings (REBs). [4] Solid lubricants such as polytetrafluoroethylene (PTFE) are used in rolling-element bearings (REBs) when conventional lubrication (i. [5] However, in industrial applications, motions with sliding or transient effects are very common for gears, rolling-element bearings or even chain drives, evaluation of the grease performance under such conditions is vital for determining the lubrication mechanism and designing new greases. [6] The roller-end/flange contact in rolling-element bearings represents a crucial design point. [7] Specifically, dimeric Notch1 transcriptional complexes activate Rag1 and Rag2 through a novel cis-element bearing a sequence-paired site (SPS). [8] hexapods) either have a limited range of motion combined with a good repeatability when comprising flexure joints, or they have limited repeatability with a large workspace when using traditional rolling- or sliding-element bearings. [9] Solid lubricants such as polytetrafluoroethylene (PTFE) are used in rolling-element bearings (REBs) when conventional lubrication (i. [10] Therefore, a PDA method based on one-dimensional sparse representation self-learning dictionary is presented to assess the degradation trend of rolling-element bearings. [11] Thermal images of rolling-element bearing in six conditions have been considered, including one healthy and five faulty conditions, and then a comparison based on classification performance has been done using shallow and deep learning approaches incorporating artificial neural network (ANN) and convolutional neural network (CNN). [12] Rolling-element bearings are commonly used in rotary machinery. [13] Rolling-element bearings are the most commonly used components in all rotating machinery. [14] Statistically, the outer race of the rolling-element bearing is the most sensitive element used in induction motors. [15] For several applications the requirements in rotational speed, bearing load and maximal vibration level are so extreme that neither rolling-element bearings nor fluid-film bearings could provide necessary operating characteristics during all regimes of operation. [16] The main aim of the study was to perform sensitivity analysis of axial rolling-element bearing by data mining algorithm. [17] Element bearings are widespread significant parts of rotary machinery in industries and their condition monitoring through fault detection and isolation methods are of superior interests to engineers. [18] The health condition of rolling-element bearings is important for machine performance and operating safety. [19] Rolling-element bearing that is mostly used wherever rotary motion is provided to a shaft in rotating machineries. [20] The methodology combines experiments with comprehensive dynamic models, and it is demonstrated with examples of rolling-element bearings, gear transmissions, and cardan joints. [21] Rolling-element bearings (REBs) faults are one of the most common breakdowns of rotating machines, thus proposing effective bearing fault diagnosis and classification methods is vital. [22] This article deals with the development of two models of rotor supported by deep groove rolling-element bearings to study the dynamic response. [23] Rolling-element bearings are crucial components in all rotating machinery, and their failure will initially degrade the machine performance, and later cause complete shutdown. [24] In this study, failure analysis of rolling-element bearing of vehicle brake tester used in a vehicle inspection station was considered according to the real time vibration signals. [25]구름 요소 베어링(REB)은 구름 요소와 베어링 레이스 사이의 미끄러짐(미끄러짐)으로 인해 심각한 손상을 일으킬 수 있습니다. [1] 이 문제를 해결하기 위해 특정 한계까지 이러한 오정렬에 직면할 수 있는 능력이 있는 구면 구름 요소 베어링이 사용됩니다. [2] 구름 요소 베어링의 유한 요소 해석과 관련된 계산 노력을 줄이기 위해 연구자들은 일반적으로 다양한 전략을 사용합니다. [3] 이 문서는 구름 요소 베어링(REB)에 대한 데이터 기반 예후 프레임워크를 제시합니다. [4] 폴리테트라플루오로에틸렌(PTFE)과 같은 고체 윤활제는 기존 윤활(i. [5] 그러나 산업 응용 분야에서 슬라이딩 또는 과도 효과가 있는 동작은 기어, 구름 요소 베어링 또는 체인 드라이브에 매우 일반적이므로 이러한 조건에서의 그리스 성능 평가는 윤활 메커니즘을 결정하고 새로운 그리스를 설계하는 데 매우 중요합니다. [6] 구름 요소 베어링의 롤러 끝단/플랜지 접촉은 중요한 설계 포인트를 나타냅니다. [7] 특히, 이량체 Notch1 전사 복합체는 SPS(서열 쌍을 이루는 부위)를 포함하는 새로운 시스 요소를 통해 Rag1 및 Rag2를 활성화합니다. [8] 헥사포드)는 굴곡 조인트를 구성할 때 우수한 반복성과 결합된 제한된 동작 범위를 갖거나 기존의 롤링 또는 슬라이딩 요소 베어링을 사용할 때 넓은 작업 공간으로 제한된 반복성을 갖습니다. [9] 폴리테트라플루오로에틸렌(PTFE)과 같은 고체 윤활제는 기존 윤활(i. [10] 따라서 구름 요소 베어링의 열화 경향을 평가하기 위해 1차원 희소 표현 자가 학습 사전을 기반으로 하는 PDA 방법이 제시됩니다. [11] 정상 상태 1개와 결함 5개를 포함하여 6가지 조건에서 구름 요소 베어링의 열화상을 고려한 다음, 인공 신경망(ANN)과 컨볼루션 신경망을 통합한 얕은 학습 및 딥 러닝 접근 방식을 사용하여 분류 성능을 기반으로 한 비교를 수행했습니다. (CNN). [12] 롤링 요소 베어링은 일반적으로 회전 기계에 사용됩니다. [13] 구름 요소 베어링은 모든 회전 기계에서 가장 일반적으로 사용되는 구성 요소입니다. [14] 통계적으로 롤링 요소 베어링의 외부 레이스는 유도 전동기에 사용되는 가장 민감한 요소입니다. [15] 여러 응용 분야의 경우 회전 속도, 베어링 하중 및 최대 진동 수준에 대한 요구 사항이 너무 극심하여 구름 요소 베어링이나 유체 필름 베어링이 모든 작동 영역에서 필요한 작동 특성을 제공할 수 없습니다. [16] 연구의 주요 목적은 데이터 마이닝 알고리즘을 통해 축방향 구름요소 베어링의 민감도 분석을 수행하는 것이었습니다. [17] 요소 베어링은 산업에서 회전 기계의 중요한 부분으로 널리 퍼져 있으며 결함 감지 및 격리 방법을 통한 상태 모니터링은 엔지니어에게 가장 큰 관심사입니다. [18] 구름 요소 베어링의 상태는 기계 성능과 작동 안전에 중요합니다. [19] 회전하는 기계의 축에 회전 운동을 가하는 모든 곳에 주로 사용되는 롤링 요소 베어링. [20] 방법론은 포괄적인 동적 모델과 실험을 결합하고 구름 요소 베어링, 기어 변속기 및 카르단 조인트의 예를 통해 시연됩니다. [21] 롤링 요소 베어링(REB) 결함은 회전 기계의 가장 일반적인 고장 중 하나이므로 효과적인 베어링 결함 진단 및 분류 방법을 제안하는 것이 중요합니다. [22] 이 기사에서는 동적 응답을 연구하기 위해 깊은 홈 구름 요소 베어링으로 지지되는 두 가지 로터 모델의 개발을 다룹니다. [23] 롤링 요소 베어링은 모든 회전 기계의 중요한 구성 요소이며 실패하면 처음에는 기계 성능이 저하되고 나중에는 완전한 정지가 발생합니다. [24] 본 연구에서는 실시간 진동 신호에 따라 차량 검사소에서 사용되는 차량 제동 시험기의 구름 요소 베어링의 고장 분석을 고려하였다. [25]
fault feature extraction 결함 특징 추출
A new fault feature extraction method for rolling element bearing is put forward in this paper based on modified Fourier mode decomposition (MFMD) and multi-scale permutation entropy, and the fault pattern recognition is studied by combining BP neural network. [1] Sparse representation based on the matching pursuit (MP) algorithm is an effective method for fault feature extraction involving rolling element bearings. [2] Finally, grey correlation analysis (GCA) is conducted to determine the optimal SE scale of MMA and achieve fault feature extraction of rolling element bearing. [3] K-singular value decomposition (K-SVD), as an extension of sparse coding, has attracted great attention for fault feature extraction of rolling element bearings (REBs) in recent years. [4] In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. [5]본 논문에서는 MFMD(Modified Fourier Mode Decomposition)와 다중 스케일 순열 엔트로피를 기반으로 구름 요소 베어링에 대한 새로운 결함 특징 추출 방법을 제시하고 BP 신경망을 결합하여 결함 패턴 인식을 연구합니다. [1] MP(매칭 추적) 알고리즘을 기반으로 하는 희소 표현은 롤링 요소 베어링과 관련된 결함 특징 추출에 효과적인 방법입니다. [2] 마지막으로 그레이 상관 분석(GCA)을 수행하여 MMA의 최적 SE 스케일을 결정하고 구름 요소 베어링의 결함 특성 추출을 달성합니다. [3] K-특이값 분해(K-SVD)는 희소 코딩(sparse coding)의 확장으로서 최근 몇 년 동안 롤링 요소 베어링(REB)의 결함 특성 추출에 큰 관심을 받았습니다. [4] nan [5]
fault diagnosis method
In order to make up for the deficiency of traditional single diagnosis in rolling element bearing fault diagnosis application, eliminate a large amount of redundant information and improve the classification effect of the aliasing mode, based on comprehensive analysis of the respective advantages of fuzzy set and tree search, this paper presents a joint rolling bearing fault diagnosis method based on tree-inspired feature selection and FS-DFV (Fuzzy Set and Dependent Feature Vector). [1] This paper proposes a novel deep learning-based fault diagnosis method for rolling element bearings. [2] In this paper, a set of novel smart timely-monitoring fault-diagnosis method is designed, which utilize the characteristics and mapping of the chaos system, provides a high efficiency and accurate identification strategy for the normal and abnormal states of rolling-element bearing. [3] Owing to the problem of the incipient fault characteristics being difficult to be extracted from the raw vibration signal of rolling element bearing, based on the empirical mode decomposition and kurtosis criteria, a fault diagnosis method for rolling element bearing is proposed by reducing rolling element bearing foundation vibration and noise-assisted vibration signal analysis. [4] Fault diagnosis method based on blind source separation (BSS) of rotating machinery, such as rolling element bearings and gears is a necessary tool to prevent any unexpected accidents. [5]구름 요소 베어링 결함 진단 응용 프로그램에서 전통적인 단일 진단의 결점을 보완하기 위해 퍼지 세트 및 트리 각각의 장점에 대한 포괄적인 분석을 기반으로 많은 양의 중복 정보를 제거하고 앨리어싱 모드의 분류 효과를 개선합니다. 검색을 통해 본 논문은 나무에서 영감을 받은 특징 선택과 FS-DFV(Fuzzy Set and Dependent Feature Vector)를 기반으로 한 조인트 구름 베어링 결함 진단 방법을 제시합니다. [1] 이 논문은 구름 요소 베어링에 대한 새로운 딥 러닝 기반 결함 진단 방법을 제안합니다. [2] nan [3] nan [4] nan [5]
remaining useful life 남은 유효 수명
This would help assess degradation in rolling element bearings and make prediction on the remaining useful life. [1] Spalling caused by fatigue is the most common reason for rolling element bearing failure, and spall size can be a good indicator to predict the remaining useful life of the bearing. [2]이것은 구름 요소 베어링의 성능 저하를 평가하고 남은 유효 수명을 예측하는 데 도움이 됩니다. [1] 피로로 인한 스폴링은 전동체 베어링 파손의 가장 흔한 원인이며 스폴 크기는 베어링의 남은 사용 수명을 예측하는 좋은 지표가 될 수 있습니다. [2]
Rolling Element Bearing 롤링 요소 베어링
Rolling element bearing (REB) is a critical component in any rotating machinery. [1] This would help assess degradation in rolling element bearings and make prediction on the remaining useful life. [2] This paper presents the development of an artificial neural network (ANN) model for rolling element bearings fault classification that uses features extracted from acceleration data collected during run-to-failure experiments. [3] Two cases of fault classification of rolling element bearings are used to validate the proposed method. [4] A simulation study and a real application to rolling element bearings are provided to illustrate the effectiveness of the proposed method in practice. [5] To address this problem, a fault diagnosis method for rolling element bearing was proposed using the transmission path elimination based enhanced variational mode decomposition (TPE-EVMD). [6] An improved wavelet cross spectrum (IWCS) scheme is proposed in this paper for the health monitoring of the rolling element bearings. [7] Rolling element bearings are essential components in rotary machinery and tend to have slip during motion diverting from the ideal rolling motion. [8] In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. [9] The SMA-AGDE’s performance is evaluated on the CEC’17 test suite, three engineering design problems - tension/compression spring, pressure vessel, and rolling element bearing - and two combinatorial optimization problems - bin packing and quadratic assignment. [10] Therefore, the failure of rolling element bearings should be detected early while the machine fault is small. [11] Rolling element bearing failure is one of the commonly explained reason for machine breakdown. [12] Rolling element bearings are found in every piece of machinery and are therefore a source of energy losses that cannot be ignored. [13] From a kinematic point of view, rolling elements should continuously roll on the raceways of rolling element bearings. [14] CMS can correctly pick up the transient features from interference signals and can accurately recognize the rolling element bearing defect. [15] Vibration data is measured near to bearings, which themselves are monitored for their condition, and hence rolling element bearing (REB) is the focus of this study. [16] The operation of concentrated contacts observed in gearboxes, rolling element bearings, and cams/followers of valve etc of several machinery and mechanical systems in the mixed-elastohydrodynamic lubrication (mixed-EHL) regime is one of the key reasons for occurring the incident of surface contact fatigue. [17] This chapter presents an application of health index (HI) for fault diagnosis of rolling element bearing (REB) which has been successfully used in diverse fields such as image processing, prognostic health management (PHM), and involves integration of mathematical and statistical concepts. [18] Envelope demodulation of vibration signals is surely one of the most successful methods of analysis for highlighting diagnostic information of rolling element bearings incipient faults. [19] Spalling caused by fatigue is the most common reason for rolling element bearing failure, and spall size can be a good indicator to predict the remaining useful life of the bearing. [20] Rolling element bearings are among the most important components of rotating machinery, being the interface between the stationary and the rotating parts. [21] The model was trained and tested on two publicly available and widely studied vibration datasets for rolling element bearing faults. [22] The dynamic behavior of rolling element bearings has been a complex phenomenon in high speed rotating machinery. [23] Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. [24] Anomaly detection is the cornerstone for the health management of rolling element bearings. [25] The normal operation of rotating machineries depends on the health conditions of rolling element bearings. [26] The proposed approach is experimentally validated with two case studies on rolling element bearings, and comparisons with other state-of-the-art techniques are also presented. [27] Finally, the proposed method is applied to fault diagnosis of rolling element bearings, and experimental analysis results show that the proposed method can recognize different failure types accurately and effectively with only ten time-domain dimensional parameters and a small quantity of training samples. [28] Time histories of the displacements, their Fast Fourier transforms (FFT) and axis orbits at different positions of the plain journal bearing, the impeller and the rolling element bearing under different operating conditions are analyzed. [29] Rolling element bearings are one of the key components of many rotating machines. [30] The vibration caused by an early defect on the rolling element bearing (REB) is very weak and easy to be submerged other signals and noise. [31] For rolling element bearings, its life is one of the most crucial considerations. [32] For rolling element bearings, a measure of satisfactory performance is a long life, lubrication and thermal characteristics. [33] Proper functioning of rolling element bearings is critical to ensuring reliable and safe power transmission. [34] In a case study on rolling element bearings, the SC-GAN is verified to be able to generate raw vibration signals under 10 different health conditions with a more stable training process than other models. [35] This paper presents a new deep learning approach for failure prognostics of rolling element bearings based on a Long Short-Term Memory (LSTM) predictor trained simultaneously within a Generative Adversarial Network (GAN) architecture. [36] The diagnosis of early-stage defects of rolling element bearings (REBs) using vibration signals is a very difficult task since bearing fault signals are usually weak and masked by shaft rotating signals, gear meshing signals, and strong background noise. [37] The vibration response of rolling element bearing has a close relation with its fault. [38] The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings. [39] A defective rolling element bearing produces a specific vibration pattern that can be used as a diagnostic tool in predictive maintenance. [40] Finally, two rolling element bearing experiments including a double row bearing run-to-failure experiment and a high-speed train bearing experiment were performed to demonstrate the feasibility and effectiveness of the proposed method in mechanical incipient fault diagnosis. [41] Since fatigue life is the pivotal parameter in rolling element bearings, an enhancement in fatigue life has been enviable always. [42] Rolling element bearings are extremely important components of rotating machines, and bearing defects can cause machines to fail. [43] In the experiments, the Data Guardian is tested under the attacks to the fault diagnosis models on Tennessee Eastman process (TEP) and rolling element bearing (REB) from Case Western Reserve University. [44] A new fault feature extraction method for rolling element bearing is put forward in this paper based on modified Fourier mode decomposition (MFMD) and multi-scale permutation entropy, and the fault pattern recognition is studied by combining BP neural network. [45] Rolling Element Bearing (REB) fault diagnosis is a widely researched theme. [46] High-speed rolling element bearings for aircraft engines are custom-made components and operate under high temperature conditions owing to the elevated rotational speeds and loads. [47] This study addresses the high level of misdiagnoses and low reliability of individual rolling element bearing fault diagnosis methods by proposing a fault diagnosis scheme with enhanced diagnosis accuracy that combines the results of two individual diagnosis methods based on an improved information fusion method. [48] Intelligent fault diagnosis of rolling element bearings gains increasing attention in recent years due to the promising development of artificial intelligent technology. [49] Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. [50]롤링 요소 베어링(REB)은 모든 회전 기계에서 중요한 구성 요소입니다. [1] 이것은 구름 요소 베어링의 성능 저하를 평가하고 남은 유효 수명을 예측하는 데 도움이 됩니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] 구름 요소 베어링 고장은 일반적으로 기계 고장의 원인 중 하나로 설명됩니다. [12] nan [13] nan [14] CMS는 간섭 신호에서 과도 기능을 올바르게 선택하고 구름 요소 베어링 결함을 정확하게 인식할 수 있습니다. [15] nan [16] nan [17] nan [18] nan [19] 피로로 인한 스폴링은 전동체 베어링 파손의 가장 흔한 원인이며 스폴 크기는 베어링의 남은 사용 수명을 예측하는 좋은 지표가 될 수 있습니다. [20] nan [21] 이 모델은 구름 요소 베어링 결함에 대해 공개적으로 사용 가능하고 널리 연구된 두 가지 진동 데이터 세트에서 훈련 및 테스트되었습니다. [22] nan [23] nan [24] nan [25] nan [26] nan [27] nan [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] nan [38] nan [39] nan [40] nan [41] nan [42] nan [43] nan [44] 본 논문에서는 MFMD(Modified Fourier Mode Decomposition)와 다중 스케일 순열 엔트로피를 기반으로 구름 요소 베어링에 대한 새로운 결함 특징 추출 방법을 제시하고 BP 신경망을 결합하여 결함 패턴 인식을 연구합니다. [45] nan [46] nan [47] 본 연구에서는 개선된 정보 융합 방법을 기반으로 두 가지 개별 진단 방법의 결과를 결합하여 진단 정확도가 향상된 결함 진단 기법을 제안하여 개별 전동체 베어링 결함 진단 방법의 높은 오진과 낮은 신뢰도를 해결합니다. [48] nan [49] nan [50]
element bearing fault 요소 베어링 결함
The model was trained and tested on two publicly available and widely studied vibration datasets for rolling element bearing faults. [1] This study addresses the high level of misdiagnoses and low reliability of individual rolling element bearing fault diagnosis methods by proposing a fault diagnosis scheme with enhanced diagnosis accuracy that combines the results of two individual diagnosis methods based on an improved information fusion method. [2] Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. [3] The proposed method was based on the application of some mode decomposition technique in order to extract monocomponent signals from the vibration and to calculate an indicator of the modulation produced by the rolling element bearing fault. [4] In order to overcome the problems of low accuracy in rolling element bearing fault identification, a new index named envelope spectrum sparse ration (ESSR) based on the sparse representation of envelope spectrum is proposed. [5] However, it is limited in diagnosing rolling element bearing fault in the case of the algorithm iteration period is unknown. [6] The development of rolling element bearing fault diagnosis systems has attracted a great deal of attention due to bearing components having a high tendency toward unexpected failures. [7] A powerful and popular method for rolling element bearing fault detection is the envelope analysis which is presented next. [8] Rolling element bearing faults of a laboratory scale wind turbine gearbox operating under nonstationary loads have been diagnosed using condition monitoring (CM) techniques such as vibration analysis, acoustic analysis, and lubrication oil analysis. [9] In order to make up for the deficiency of traditional single diagnosis in rolling element bearing fault diagnosis application, eliminate a large amount of redundant information and improve the classification effect of the aliasing mode, based on comprehensive analysis of the respective advantages of fuzzy set and tree search, this paper presents a joint rolling bearing fault diagnosis method based on tree-inspired feature selection and FS-DFV (Fuzzy Set and Dependent Feature Vector). [10] 14 | 0:00:00 Start 0:01:24 Syllabus - Winter Term 2018/19 0:01:50 Variants of Anomaly Detection Problem 0:03:24 Unsupervised Anomaly Detection 0:04:34 Statistical Outlier Detection 0:07:21 Classification Based Techniques 0:17:31 Manipulating Data Records 0:21:01 Predictive Maintenance Example in MATLAB 0:22:33 Predicitive Maintance Done by Humans 0:23:44 References for Today's Class 0:24:05 Predictive Maintenance Software 0:25:46 Where to Find Data Analyze? (and Play with) 0:27:34 Recap Resources 0:28:01 Rolling Element Bearing Fault Diagnosis 0:28:46 Ball Pass Frequency 0:29:48 Rolling Element Bearing Fault Diagnosis 0:30:04 MFPT Data Set 0:30:28 Inner Race Fault Data 0:30:50 Time Domain - Closer Lock 0:31:08 Single Envelope Spectrum Analysis 0:31:46 MFPT Data Set 0:38:46 MATLAB Examples. [11] Extraction of the fault related impulses from the raw vibration signal is important for rolling element bearing fault diagnosis. [12] In connection to the former mentioned a prototype was developed and tested for purposes of simulated rolling element bearing fault systems signals with appropriate fault diagnostic and analytics. [13] The squared envelope spectrum (SES) is one of the most effective methods in rolling element bearing fault diagnosis. [14]이 모델은 구름 요소 베어링 결함에 대해 공개적으로 사용 가능하고 널리 연구된 두 가지 진동 데이터 세트에서 훈련 및 테스트되었습니다. [1] 본 연구에서는 개선된 정보 융합 방법을 기반으로 두 가지 개별 진단 방법의 결과를 결합하여 진단 정확도가 향상된 결함 진단 기법을 제안하여 개별 전동체 베어링 결함 진단 방법의 높은 오진과 낮은 신뢰도를 해결합니다. [2] nan [3] nan [4] nan [5] 그러나 알고리즘 반복 주기를 알 수 없는 경우 전동체 베어링 결함을 진단하는데 한계가 있다. [6] 전동체 베어링 결함 진단 시스템의 개발은 예기치 않은 고장에 대한 높은 경향이 있는 베어링 부품으로 인해 많은 관심을 받았습니다. [7] nan [8] nan [9] 구름 요소 베어링 결함 진단 응용 프로그램에서 전통적인 단일 진단의 결점을 보완하기 위해 퍼지 세트 및 트리 각각의 장점에 대한 포괄적인 분석을 기반으로 많은 양의 중복 정보를 제거하고 앨리어싱 모드의 분류 효과를 개선합니다. 검색을 통해 본 논문은 나무에서 영감을 받은 특징 선택과 FS-DFV(Fuzzy Set and Dependent Feature Vector)를 기반으로 한 조인트 구름 베어링 결함 진단 방법을 제시합니다. [10] nan [11] nan [12] nan [13] nan [14]
element bearing defect 요소 베어링 결함
CMS can correctly pick up the transient features from interference signals and can accurately recognize the rolling element bearing defect. [1] The rolling element bearing defect recognition results show that the proposed approach can effectively detect the transient characteristics and distinguish the rolling element bearing localized defect. [2] This work has been developed within the framework of non-stationary rotating machinery surveillance with emphasis on the detection of rolling element bearing defects. [3]CMS는 간섭 신호에서 과도 기능을 올바르게 선택하고 구름 요소 베어링 결함을 정확하게 인식할 수 있습니다. [1] 전동체 베어링 결함 인식 결과는 제안된 접근 방식이 과도 특성을 효과적으로 감지하고 전동체 베어링 국부적 결함을 구별할 수 있음을 보여줍니다. [2] nan [3]
element bearing failure 요소 베어링 실패
Rolling element bearing failure is one of the commonly explained reason for machine breakdown. [1] Spalling caused by fatigue is the most common reason for rolling element bearing failure, and spall size can be a good indicator to predict the remaining useful life of the bearing. [2] Discriminative feature extraction and rolling element bearing failure diagnostics are very important to ensure the reliability of rotating machines. [3]구름 요소 베어링 고장은 일반적으로 기계 고장의 원인 중 하나로 설명됩니다. [1] 피로로 인한 스폴링은 전동체 베어링 파손의 가장 흔한 원인이며 스폴 크기는 베어링의 남은 사용 수명을 예측하는 좋은 지표가 될 수 있습니다. [2] nan [3]
element bearing dataset 요소 베어링 데이터 세트
Finally, experiments on the public rolling element bearing dataset show that our method can significantly improve latency and save bandwidth while ensuring accuracy. [1] The experiments on the real-world rolling element bearing dataset are carried out for validation, and promising testing accuracies is achieved in different tasks, which are higher than the other popular methods. [2]마지막으로 공공 전동체 베어링 데이터 세트에 대한 실험은 우리의 방법이 정확도를 보장하면서 대기 시간을 크게 개선하고 대역폭을 절약할 수 있음을 보여줍니다. [1] 실제 롤링 요소 베어링 데이터 세트에 대한 실험이 검증을 위해 수행되며, 다른 인기 있는 방법보다 높은 다양한 작업에서 유망한 테스트 정확도가 달성됩니다. [2]