scholarly journals New Technique for Evaluation of Global Vibration Levels in Rolling Bearings

2002 ◽  
Vol 9 (4-5) ◽  
pp. 225-234 ◽  
Author(s):  
Rui Gomes Teixeira de Almeida ◽  
Silmara Alexandra da Silva Vicente ◽  
Linilson Rodrigues Padovese
Keyword(s):  
Fault Diagnosis ◽  
New Technologies ◽  
Residual Energy ◽  
New Technique ◽  
Mechanical Equipment ◽  
Rolling Bearings ◽  
Global Parameter ◽  
Spectrum Density ◽  
The Difference ◽  
Vibration Parameters

In the last years new technologies and methodologies have been developed for increasing the reliability of fault diagnosis in mechanical equipment, mainly in rotating machinery. Global vibration indexes as RMS, Kurtosis, etc., are widespread known in industry and in addition, are recommended by international norms. Despite that, these parameters do not allow reaching reliable equipment condition diagnosis. They are attractive for their apparent simplicity of interpretation. This work presents a discussion about the diagnosis possibilities based on these traditional parameters. The database used comprises rolling bearings vibration signals taking into account different fault conditions, several shaft speeds and loading. The obtained results show that these global vibration parameters are limited regarding correct fault diagnosis, especially in initial faults condition. As an alternative method a new technique is proposed. This technique seeks to obtain a global parameter that makes better characterization of fault condition. This methodology, named Residual Energy, uses integration of the difference between the power spectrum density of the fault condition and the normal one. The results obtained with this technique are compared with the traditional RMS and Kurtosis.

Research and Application on Fault Diagnosis for Steam Turbine Using Multi-Parameter Fusion

2014 ◽  
Vol 543-547 ◽  
pp. 1068-1073
Author(s):  
Xiao Wen Deng ◽  
Zhen Yu Zhou ◽  
Lei Song ◽  
Peng Li ◽  
Yu Jiong Gu
Keyword(s):  
Fault Diagnosis ◽  
Steam Turbine ◽  
Comprehensive Evaluation ◽  
Mechanical Equipment ◽  
Operational Parameters ◽  
Multiple Parameters ◽  
Equipment Operation ◽  
State Evaluation ◽  
Fault Mode ◽  
Vibration Parameters

For the running situation of supercritical and ultra-supercritical steam turbine unit, a fault diagnosis method of steam turbine based on multiple parameters fusion is proposed. The association of the fault mode with the vibration parameters, the thermodynamic parameters and the operational parameters is built, according to fault development mechanism, equipment operation data and professional experience. The overall state evaluation of mechanical equipment is given, the reliability of fault diagnosis is improved, and the need of the steam turbine fault diagnosis is met, by means of comprehensive evaluation of multiple parameters. Example applications verify this method.

scholarly journals Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Yuan ◽  
Xing Zhao ◽  
Jiyou Fei ◽  
Yulong Zhao ◽  
Jiahui Wang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Early Stage ◽  
Fractal Theory ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Rolling Bearings ◽  
Common Component ◽  
Time Frequency ◽  
Multifractal Method

The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid typeJand order parameterqin algorithm, the value range ofJand the cut-off condition ofqwere optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation

2021 ◽  
Author(s):  
Chenhui Qian ◽  
Quansheng Jiang ◽  
Yehu Shen ◽  
Chunran Huo ◽  
Qingkui Zhang
Keyword(s):  
Fault Diagnosis ◽  
Joint Distribution ◽  
Marginal Distribution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Abstract Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the diagnosis of rolling bearings under complex conditions. In this paper, a feature transfer learning model based on improved DenseNet and joint distribution adaptation (FT-IDJ) is proposed. With this model, we apply it to implement rolling bearing fault diagnosis. A lightweight DenseNet model is firstly proposed to extract the transferable features of the raw vibration signal. Furthermore, the parameters in the DenseNet are constrained by the domain adaptive regularization term and pseudo label learning. The marginal distribution discrepancy and the conditional distribution discrepancy of the learned transferable features are reduced by this way. The proposed method is validated by the diagnosis experiments with CWRU and Jiangnan University rolling bearing datasets. The experimental results showed that the proposed FT-IDJ has higher classification accuracy than DAN and other eight methods, which demonstrated its effectively learning transferable features from auxiliary data.

Stress Field Around a Large Opening in a Pressure Vessel During a Major Repair

2009 ◽  
Author(s):  
Erik Garrido ◽  
Euro Casanova
Keyword(s):  
Pressure Vessel ◽  
New Technologies ◽  
Mechanical Design ◽  
General Purpose ◽  
Pressure Vessels ◽  
Mechanical Equipment ◽  
Stress Distributions ◽  
Large Opening ◽  
Von Mises ◽  
Case Basis

It is a regular practice in the oil industry to modify mechanical equipment to incorporate new technologies and to optimize production. In the case of pressure vessels, it is occasionally required to cut large openings in their walls in order to have access to the interior part of the equipment for executing modifications. This cutting process produces temporary loads, which were obviously not considered in the original mechanical design. Up to now, there is not a general purpose specification for approaching the assessments of stress levels once a large opening in a vertical pressure vessel has been made. Therefore stress distributions around large openings are analyzed on a case-by-case basis without a reference scheme. This work studies the distribution of the von Mises equivalent stresses around a large opening in FCC Regenerators during internal cyclone replacement, which is a frequently required practice for this kind of equipment. A finite element parametric model was developed in ANSYS, and both numerical results and illustrating figures are presented.

scholarly journals An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 617
Author(s):  
Jianpeng Ma ◽  
Shi Zhuo ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guangzhu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Lévy Noise ◽  
Model Parameters ◽  
Rolling Bearings ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Weak Fault ◽  
Levy Noise ◽  
The Impact

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

Fault diagnosis method of rolling bearings based on VMD and MDSVM

2021 ◽  
Author(s):  
MeiYing Qiao ◽  
XiaXia Tang ◽  
YuXiang Liu ◽  
ShuHao Yan
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearings ◽  
Diagnosis Method

scholarly journals A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3382
Author(s):  
Zhongwei Zhang ◽  
Mingyu Shao ◽  
Liping Wang ◽  
Sujuan Shao ◽  
Chicheng Ma
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Class Imbalance ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Mechanical Equipment ◽  
Reliable Operation ◽  
Cross Domain ◽  
Geometric Difference ◽  
Model Generalization

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

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