Dynamic asymptotic model of rolling bearings with a pitting fault based on fractional damping

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yunlong Li ◽  
Zhinong Li ◽  
Dong Wang ◽  
Zhike Peng
Keyword(s):  
Fractional Calculus ◽  
Dynamic Characteristics ◽  
Rolling Bearing ◽  
Integral Calculus ◽  
Rolling Bearings ◽  
Fractional Damping ◽  
Content Type ◽  
Asymptotic Models ◽  
Proposed Model ◽  
Different Parts

PurposeThe purpose of this paper is to discuss the asymptotic models of different parts with a pitting fault in rolling bearings.Design/methodology/approachFor rolling bearings with a pitting fault, the displacement deviation between raceways and rolling elements is usually considered to vary instantaneously. However, the deviation should change gradually. Based on this shortcoming, the variation rule and calculation method of the displacement deviation are explored. Asymptotic models of different parts with a pitting fault are discussed, respectively. Besides, rolling bearing systems have prominent fractional characteristics unconsidered in the traditional models. Therefore, fractional calculus is introduced into the modeling of rolling bearings. New dynamic asymptotic models of different parts with a pitting fault are proposed based on fractional damping. The numerical simulation is performed based on the proposed model, and the dynamic characteristics are analyzed through the bifurcation diagrams, trajectory diagrams and frequency spectrograms.FindingsCompared with the model based on integral calculus, the proposed model can better reflect the periodic characteristics and fault characteristics of rolling bearings. Finally, the proposed model is verified by the experiment. The dynamic characteristics of rolling bearings at different rotating speeds are analyzed. The experimental results are consistent with the simulation results. Therefore, the proposed model is effective.Originality/value(1) The above models are idealized, i.e. the local pitting fault is treated as a rectangle. When a component comes into contact with the fault, the displacement deviation between the component and the fault component immediately releases if the component enters the fault area and restores if the component leaves. However, the displacement deviation should change gradually. Only when the component touches the fault bottom, the displacement deviation reaches the maximum. (2) Due to the material's memory and fluid viscoelasticity, rolling bearing systems exhibit significant fractional characteristics. However, the above models are all proposed based on integral calculus. Integral calculus has some local characteristics and is not suitable for describing historical dependent processes. Fractional calculus can better describe the essential characteristics of the system.

Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Defeng Lv ◽  
Huawei Wang ◽  
Changchang Che
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Decision Fusion ◽  
Fusion Model ◽  
Vibration Signals ◽  
Content Type ◽  
Proposed Model

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

Intern-turn fault modeling and diagnosis in permanent magnet vernier machine using modified magnetic equivalent circuit method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohsen Rostami ◽  
Peyman Naderi ◽  
Abbas Shiri
Keyword(s):  
Permanent Magnet ◽  
Equivalent Circuit ◽  
Saturation Effect ◽  
Geometrical Parameters ◽  
System Of Equations ◽  
Simple Modification ◽  
Content Type ◽  
Magnetic Equivalent Circuit ◽  
Proposed Model ◽  
Different Parts

Purpose The aim of this paper is to propose the model for analyzing the electromagnetic performances of permanent magnet vernier machines (PMVMs) under healthy and faulty conditions. Design/methodology/approach The model uses interconnected reluctance network formed based on the geometrical approximations to predict magnetic performances of the machine. The network consists of several types of reluctances for modeling different parts of machine. Applying Kirchhoffs laws in the network and the machine windings, magnetic and electrical equations are obtained, respectively. To construct the model system of equations, the electrical equation is converted into algebraic form by using the trapezoidal technique. Moreover, the system of equations must be solved by Newton–Raphson method in each step-time because of considering the core saturation effect. Findings The proposed model is developed based on the modified magnetic equivalent circuit (MEC) method, in which the number of flux paths in different parts of the machine can be arbitrary selected. The saturation effect, skewed slots, the desired machine geometrical parameters and various winding arrangements are included in the proposed model; therefore, it can evaluate the time and space harmonics in modeling the PMVMs. Furthermore, a pattern for inter-turn fault detection is extracted from the stator current spectrum. Finally, 2 D-finite element method (FEM) and 3 D-FEM analysis are carried out to evaluate and verify the results of the proposed MEC model. Originality/value Generally, the element numbers have important role in modeling the machine and calculating its performance. Hence, the proposed MEC model’s capability to choose desired number of flux paths is advantage of this paper. Moreover, the developed MEC can be used for analyzing several electrical machines, including other types of vernier machines, with simple modification.

Design a degradation condition monitoring system scheme for rolling bearing using EMD and PCA

2017 ◽  
Vol 117 (4) ◽  
pp. 713-728 ◽  
Author(s):  
Jun Wu ◽  
Chaoyong Wu ◽  
Yaqiong Lv ◽  
Chao Deng ◽  
Xinyu Shao
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Rolling Bearing ◽  
Sensor Data ◽  
Health Index ◽  
Rolling Bearings ◽  
Content Type ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring System ◽  
Monotonicity Analysis

Purpose Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing. Design/methodology/approach The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index. Findings The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application. Research limitations/implications Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available. Practical implications The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics. Originality/value The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

scholarly journals RESEARCH OF THE WEAR MECHANISM OF BEARING SEATS OF AUTOMOTOR-TRACTOR TRANSMISSIONS

2021 ◽  
Vol 16 (4) ◽  
pp. 71-79
Author(s):  
Yuriy Ivanschikov ◽  
Vasiliy Skovorodin ◽  
Yuriy Dobrohotov ◽  
Roman Andreev ◽  
Aleksandr Vasil'ev ◽  
...  
Keyword(s):  
Rolling Bearing ◽  
Practical Implementation ◽  
Main Role ◽  
Fretting Corrosion ◽  
Rolling Bearings ◽  
Limiting State ◽  
Interference Fit ◽  
Proposed Model ◽  
Seating Surface ◽  
Analytical Expressions

A significant number (up to 25%) of failures of automotive transmissions are associated with a loss of rigidity in the rolling bearings. One of the main reasons for the loss of stiffness in rolling bearings is a violation of the tightness of the bearing on the shaft and in the housing due to fretting corrosion. The paper presents the results of a study of the causes of fretting corrosion in bearing fits and the patterns of its development. The conditions promoting the occurrence of the fretting process are determined and the factors characterizing the nature and intensity of destruction of contacting surfaces during fretting corrosion are established. It was also found that the greatest influence on the occurrence and course of the fretting process is exerted by the specific load on the contact surface, the duration and frequency of its application, and the amplitude of the relative slip of the contacting surfaces. Analytical expressions for calculating the actual values of the listed factors of the fretting process in the coupling of the rolling bearing with the shaft are determined by the calculation method, and a mathematical model of its destruction is proposed. For the practical implementation of the described mechanism of destruction of the rolling bearing landing on the shaft as a result of fretting corrosion, an algorithm and a program have been developed to determine the limiting state of the bearing landings of automotive transmissions. Subsequent laboratory tests confirmed the adequacy of the proposed model. As an example, the results of modeling the limiting state of the rolling bearing of the intermediate shaft of the gearbox of the K-700A tractor and the ball bearing 313 are given. It is revealed that the main role in reducing the intensity of the fretting process, along with the hardness of the shaft, is played by the roughness of its seating surface. In particular, a decrease in the roughness parameters from Ra = 2.0 µm to Ra = 0.5 µm at the same hardness HRC48 and an interference fit in N = 24 µm leads to an increase in the joint resource by 1.5 times

Fault diagnosis for rolling bearing based on SIFT-KPCA and SVM

2017 ◽  
Vol 34 (1) ◽  
pp. 53-65 ◽  
Author(s):  
Yujie Cheng ◽  
Hang Yuan ◽  
Hongmei Liu ◽  
Chen Lu
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Singular Values ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Scale Invariant ◽  
Rolling Bearings ◽  
Sift Algorithm ◽  
Content Type ◽  
Scale Invariant Feature

Purpose The purpose of this paper is to propose a fault diagnosis method for rolling bearings, in which the fault feature extraction is realized in a two-dimensional domain using scale invariant feature transform (SIFT) algorithm. This method is different from those methods extracting fault feature directly from the traditional one-dimensional domain. Design/methodology/approach The vibration signal of rolling bearings is first transformed into a two-dimensional image. Then, the SIFT algorithm is applied to the image to extract the scale invariant feature vector which is highly distinctive and insensitive to noises and working condition variation. As the extracted feature vector is high-dimensional, kernel principal component analysis (KPCA) algorithm is utilized to reduce the dimension of the feature vector, and singular value decomposition technique is used to extract the singular values of the reduced feature vector. Finally, these singular values are introduced into a support vector machine (SVM) classifier to realize fault classification. Findings The experiment results show a high fault classification accuracy based on the proposed method. Originality/value The proposed approach for rolling bearing fault diagnosis based on SIFT-KPCA and SVM is highly effective in the experiment. The practical value in engineering application of this method can be researched in the future.

Modeling and experimental study of power losses in a rolling bearing

2019 ◽  
Vol 234 (8) ◽  
pp. 1332-1351 ◽  
Author(s):  
Guglielmo Giannetti ◽  
Enrico Meli ◽  
Andrea Rindi ◽  
Alessandro Ridolfi ◽  
Zhiyong Shi ◽  
...  
Keyword(s):  
High Performance ◽  
Fluid Dynamic ◽  
Rolling Bearing ◽  
Power Losses ◽  
Rolling Bearings ◽  
Proposed Model ◽  
Improved Model ◽  
Bearing Behavior ◽  
Very High ◽  
Good Agreement

Due to the growing demand for very high performance in aeronautical mechanisms and systems, particular attention must be paid on the bearing modeling and design. In this framework, a fundamental role is played by high peripheral speed and very low power losses. Looking toward this direction, this paper presents an improved model of rolling bearings able to describe the system dynamic behavior and the important effect of different kinds of power losses (friction losses, fluid dynamic losses, etc.). The proposed model is characterized by a high numerical efficiency and allows the investigation of the rolling bearing behavior both under transient and steady conditions. A comparison between the experimental and simulated results is also presented in this paper. The analysis of the results is encouraging and shows a good agreement between experiments and model simulations.

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

2020 ◽  
Vol 72 (7) ◽  
pp. 947-953 ◽  
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Xiaomei Ni ◽  
Qiang Fu
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Intelligent Fault Diagnosis ◽  
Content Type ◽  
Noise Interference ◽  
Proposed Model ◽  
Long Time ◽  
Diagnosis Method

Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN. Findings The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models. Originality/value The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

Efficiency of application of the phase-chronometric method and neurodiagnostics for monitoring the degradation of rolling bearings during operation

2020 ◽  
pp. 43-50
Author(s):  
A.S. Komshin ◽  
K.G. Potapov ◽  
V.I. Pronyakin ◽  
A.B. Syritskii
Keyword(s):  
Life Cycle ◽  
Wavelet Analysis ◽  
Metrological Support ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Measurement Information ◽  
Rolling Bearings ◽  
Vibration Diagnostics ◽  
Bearing Life ◽  
Alternative Approach

The paper presents an alternative approach to metrological support and assessment of the technical condition of rolling bearings in operation. The analysis of existing approaches, including methods of vibration diagnostics, envelope analysis, wavelet analysis, etc. Considers the possibility of applying a phase-chronometric method for support on the basis of neurodiagnostics bearing life cycle on the basis of the unified format of measurement information. The possibility of diagnosing a rolling bearing when analyzing measurement information from the shaft and separator was evaluated.

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