Rolling Bearing Safety Region Estimation Based on Information Entropy

2014 ◽  
Vol 614 ◽  
pp. 40-43
Author(s):  
Hao Jun Sun ◽  
Lei Zhang ◽  
Yong Qin
Keyword(s):  
Roller Bearing ◽  
Rolling Bearing ◽  
Singular Value ◽  
Time Interval ◽  
Spectral Entropy ◽  
Vibration Acceleration ◽  
State Identification ◽  
Power Spectral ◽  
Acceleration Data ◽  
Bearing Condition Monitoring

The basic idea of safety region is introduced into roller bearing condition monitoring. Power Spectral Entropy, Singular value Entropy are used comprehensively for the estimation of the safety region and the identification of normal state and faulty state for the roller bearing operational status. First, the vibration acceleration data was segmented according to a certain time interval and then establish Power Spectral Entropy, Singular value Entropy as characteristics of roller bearings. Finally, SVM was used for the estimation of the safety region of the roller bearing operation state, and multi-class SVM was used of the identification of the four states. The results show that both the safety region estimation and state identification are accurate, and confirm the validity of the method.

scholarly journals Safety Region Estimation and State Identification of Rolling Bearing Based on Statistical Feature Extraction

2013 ◽  
Vol 20 (5) ◽  
pp. 833-846 ◽  
Author(s):  
Yuan Zhang ◽  
Yong Qin ◽  
Zongyi Xing ◽  
Limin Jia ◽  
Xiaoqing Cheng
Keyword(s):  
Principal Component ◽  
Rolling Bearing ◽  
Least Square ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Time Interval ◽  
Intrinsic Mode Functions ◽  
Outer Race ◽  
State Identification ◽  
Bearing Condition Monitoring

The idea of safety region was introduced into the rolling bearing condition monitoring. The safety region estimation and the state identification of the rolling bearing operational were performed by the comprehensive utilization of Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), and the Least Square Support Vector Machine (LSSVM). The collected vibration data was segmented according to a certain time interval, and then the Intrinsic Mode Functions (IMFs) of each piece of the data were obtained by EMD. The control limits of two statistical variables extracted by PCA were presented as state characteristics. The safety region estimation for the rolling bearing operational status was performed by two-class LSSVM. The states of normal bearing, ball fault, inner race fault, and outer race fault were identified by the multi-class LSSVM. The results show that the estimation accuracy for both the safety region and the states identification reached 95%, and that the validity of the proposed method was verified.

scholarly journals Improving the Stochastic Model of Ionospheric Delays for BDS Long-Range Real-Time Kinematic Positioning

2021 ◽  
Vol 13 (14) ◽  
pp. 2739
Author(s):  
Huizhong Zhu ◽  
Jun Li ◽  
Longjiang Tang ◽  
Maorong Ge ◽  
Aigong Xu
Keyword(s):  
Real Time ◽  
Long Range ◽  
Ionospheric Delay ◽  
Time Interval ◽  
Short Time Interval ◽  
Dual Frequency ◽  
Triple Frequency ◽  
Ambiguity Fixing ◽  
Power Spectral ◽  
Observation Noise

Although ionosphere-free (IF) combination is usually employed in long-range precise positioning, in order to employ the knowledge of the spatiotemporal ionospheric delays variations and avoid the difficulty in choosing the IF combinations in case of triple-frequency data processing, using uncombined observations with proper ionospheric constraints is more beneficial. Yet, determining the appropriate power spectral density (PSD) of ionospheric delays is one of the most important issues in the uncombined processing, as the empirical methods cannot consider the actual ionosphere activities. The ionospheric delays derived from actual dual-frequency phase observations contain not only the real-time ionospheric delays variations, but also the observation noise which could be much larger than ionospheric delays changes over a very short time interval, so that the statistics of the ionospheric delays cannot be retrieved properly. Fortunately, the ionospheric delays variations and the observation noise behave in different ways, i.e., can be represented by random-walk and white noise process, respectively, so that they can be separated statistically. In this paper, we proposed an approach to determine the PSD of ionospheric delays for each satellite in real-time by denoising the ionospheric delay observations. Based on the relationship between the PSD, observation noise and the ionospheric observations, several aspects impacting the PSD calculation are investigated numerically and the optimal values are suggested. The proposed approach with the suggested optimal parameters is applied to the processing of three long-range baselines of 103 km, 175 km and 200 km with triple-frequency BDS data in both static and kinematic mode. The improvement in the first ambiguity fixing time (FAFT), the positioning accuracy and the estimated ionospheric delays are analysed and compared with that using empirical PSD. The results show that the FAFT can be shortened by at least 8% compared with using a unique empirical PSD for all satellites although it is even fine-tuned according to the actual observations and improved by 34% compared with that using PSD derived from ionospheric delay observations without denoising. Finally, the positioning performance of BDS three-frequency observations shows that the averaged FAFT is 226 s and 270 s, and the positioning accuracies after ambiguity fixing are 1 cm, 1 cm and 3 cm in the East, North and Up directions for static and 3 cm, 3 cm and 6 cm for kinematic mode, respectively.

scholarly journals Research on Rolling Bearing Fault Feature Extraction Based on Singular Value Decomposition considering the Singular Component Accumulative Effect and Teager Energy Operator

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Longlong Li ◽  
Yahui Cui ◽  
Runlin Chen ◽  
Lingping Chen ◽  
Lihua Wang
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Singular Value ◽  
Teager Energy Operator ◽  
Singular Component ◽  
Value Decomposition ◽  
Teager Energy

The extraction of impulsive signatures from a vibration signal is vital for fault diagnosis of rolling element bearings, which are always whelmed by noise, especially in the early stage of defect development. Aiming at the weak defect diagnosis, kurtosis of Teager energy operator (KTEO) spectrum is employed to indicate the fault information capacity of a spectrum, and considering the accumulative effect of a singular component, accumulative kurtosis of TEO (AKTEO) is firstly proposed to determine the proper signal reconstructed order during vibration signal processing using singular value decomposition (SVD). Then, a vibration processing scheme named SVD-AKTEO is designed where an iteration is employed to reflect an accumulative singular effect by kurtosis of TEO spectrum. Finally, the fault diagnosis results can be extracted from the TEO spectrum output by SVD-AKTEO. Simulation data and real data from a run-to-failure experiment of a rolling bearing are adopted to validate the efficiency, and comparative analysis demonstrates the feasibility to detect the early defect of the rolling bearing.

A degradation phase division technique based on Gath–Geva fuzzy clustering

2020 ◽  
Vol 26 (15-16) ◽  
pp. 1147-1154
Author(s):  
Bing Wang ◽  
Wang Wei ◽  
Xiong Hu ◽  
Dejian Sun
Keyword(s):  
Fuzzy Clustering ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Mechanical Equipment ◽  
Spectral Entropy ◽  
Variation Pattern ◽  
Intelligent Maintenance

In allusion to the issue of degradation feature extraction and degradation phase division, a logistic chaotic map is used to study the variation pattern of spectral entropy, and a technique based on Gath–Geva fuzzy clustering is proposed. The degradation features include spectral entropy, root mean square, and “curved time,” which are more in line with the performance degradation process than degradation time. Gath–Geva fuzzy clustering is introduced to divide different phases in the degradation process. The rolling bearing lifetime vibration signal from the intelligent maintenance systems (IMS) bearing test center was introduced for instance analysis. The results show that spectral entropy is able to effectively describe the complexity variation pattern in the performance degradation process and has some advantages in sensitivity and calculation speed. The introduced “curved time” is able to reflect the agglomeration character of the degradation condition on a time scale, which is more in line with the performance degradation pattern of mechanical equipment. Gath–Geva fuzzy clustering is able to divide the degradation phase of mechanical equipment such as bearings accurately.

A New Quasi-Static Cylindrical Roller Bearing Model to Accurately Consider Non-Hertzian Contact Pressure in Time Domain Simulations

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Simon Kabus ◽  
Michael R. Hansen ◽  
Ole Ø. Mouritsen
Keyword(s):  
Fatigue Life ◽  
Time Domain ◽  
Roller Bearing ◽  
Rolling Bearing ◽  
Elastic Half Space ◽  
Hertzian Contact ◽  
Fatigue Life Analysis ◽  
Cylindrical Roller Bearing ◽  
Six Degree Of Freedom ◽  
Cylindrical Roller

The accuracy of the fatigue life calculations in rolling bearing simulations is highly dependent on the precision of the roller-raceway contact simulations. Several different methods exist to simulate these pressure distributions and in time domain bearing simulations, where many contacts need evaluation, the simple and time efficient methods are more popular, yielding erroneous life estimates. This paper presents a new six degree of freedom frictionless quasi-static time domain cylindrical roller bearing model that uses high precision elastic half-space theory to simulate the contact pressures. The potentially higher computational demand using the advanced contact calculations is addressed by preprocessing a series of contacts at different centerline approaches and roller tilt angles, which are used for interpolating contact results during time domain simulations. It is demonstrated that this new model allows for simulation of bearing misalignments, roller centrifugal forces, and flange contact induced roller tilt moments, and that the effect of these conditions is directly evaluated in a detailed fatigue life analysis. Finally, the stiffness of the bearing model is validated against existing experimental data with good correlation.

Fault feature extraction and classification based on WPT and SVD: Application to element bearings with artificially created faults under variable conditions

2016 ◽  
Vol 231 (22) ◽  
pp. 4186-4196 ◽  
Author(s):  
Mourad Kedadouche ◽  
Zhaoheng Liu
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Support Vector ◽  
Rolling Bearings ◽  
The Matrix ◽  
Value Decomposition

Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.

TREMORX: AN ASSISTIVE DEVICE TO IMPROVE TREMOR PATIENT WRITING CAPABILITY

2016 ◽  
Vol 78 (6-11) ◽  
Author(s):  
Z. M. Yusop ◽  
M. Z. Md. Zain ◽  
M. Hussein ◽  
A. R. Musa
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Assistive Device ◽  
Significant Response ◽  
Matlab Simulation ◽  
Hand Tremor ◽  
Power Spectral ◽  
Acceleration Data ◽  
Frequency Domains ◽  
Time And Frequency Domains

Patients with hand tremor often face difficulties in performing daily tasks using their hands, especially writing. Hence, this paper presents an inventive assistive device named as TREMORX that can assist patients in overcoming this situation by improving their handwriting legibility. TREMORX is designed to suit the patient’s handgrip and focuses more on the patient's comfort. The aim is to analyze the performance of the device based on acceleration data by implementing the power spectral density (PSD), which is focused on reduction of coherence amplitude, and displaying the signal in the time and frequency domains. The measured acceleration data are further analyzed with Matlab simulation and show a significant response. The TREMORX is designed in three different lengths of diameter and the stiffness of each one is measured. The effectiveness of the device is identified using the force sensitive resistor (FSR) to measure the force and flex sensor, as to determine the deflection. The results indicate that the patients experienced improvements in handwriting quality when using TREMORX.

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