scholarly journals Recognition of Rolling Bearing Life Status Based on FNER Performance Degradation Indicator and IDRSN

2021 ◽  
Vol 57 (15) ◽  
pp. 105
Keyword(s):  
Rolling Bearing ◽  
Performance Degradation ◽  
Bearing Life ◽  
Degradation Indicator

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.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

scholarly journals Estimation of rolling bearing life with damage curve approach

2011 ◽  
Vol 18 (3) ◽  
pp. 66-70 ◽  
Author(s):  
H. Mehdigholi ◽  
H. Rafsanjani ◽  
Behzad Mehdi
Keyword(s):  
Natural Frequency ◽  
Damage Mechanics ◽  
Calculated Data ◽  
Life Time ◽  
Rolling Bearing ◽  
Vibratory System ◽  
Outer Race ◽  
Bearing Life ◽  
Proposed Model ◽  
Acceleration Amplitude

Estimation of rolling bearing life with damage curve approach The ability to determine the bearing life time is one of the main purposes in maintenance of rotating machineries. Because of reliability, cost and productivity, the bearing life time prognostic is important. In this paper, a stiffness-based prognostic model for bearing systems is discussed. According to presumed model of bearing and fundamental of damage mechanics, damage curve approach is used to relate stiffness of vibratory system and bearing running life. Furthermore, using the relation between acceleration amplitude at natural frequency and stiffness, final relation between acceleration amplitude at natural frequency and running life time according to damage curve approach can be established and the final running time is predicted. Experiments have been performed on self alignment bearing under failures on inner race and outer race to calibrate and to validate the proposed model. The comparison between model-calculated data and experimental results indicates that this model can be used effectively to predict the failure lifetime and the remaining life of a bearing system.

scholarly journals Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3402 ◽  
Author(s):  
Md Arafat Habib ◽  
Akhand Rai ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Concrete Beams ◽  
Early Stage ◽  
Concrete Structures ◽  
Feature Selection Method ◽  
Noise Removal ◽  
Performance Degradation ◽  
Reinforced Concrete Beams ◽  
Chebyshev Inequality ◽  
Degradation Indicator

Acoustic emission (AE) has been used extensively for structural health monitoring based on the stress waves generated due to evolution of cracks in concrete structures. A major concern while using AE features is that each of them responds differently to the fractures in concrete structures. To tackle this problem, Mahalanobis—Taguchi system (MTS) is utilized, which fuses the AE feature space to provide comprehensive and reliable degradation indicator with a feature selection method to determine useful features. Further, majority of the existing investigations gave little attention to naturally occurring cracks, which are actually more difficult to detect. In this study, a novel degradation indicator (DI) based on AE features and MTS is proposed to indicate the performance degradation in reinforced concrete beams. The experimental results confirm that the MTS can successfully distinguish between healthy and faulty conditions. To alleviate the noise from the DI obtained through MTS, a noise-removal strategy based on Chebyshev inequality is suggested. The results show that the proposed DI based on AE features and MTS is capable of detecting early stage cracks as well as development of damage in concrete beams.

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.

scholarly journals Method for Determining Starting Point of Rolling Bearing Life Prediction Based on Linear Regression

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 923
Author(s):  
Gao ◽  
Lv ◽  
Wu ◽  
Si ◽  
Hu
Keyword(s):  
Linear Regression ◽  
Life Prediction ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Linear Regression Method ◽  
Sliding Windows ◽  
Bearing Life ◽  
Starting Point ◽  
Threshold Mechanism ◽  
Gradient Domain

Aimed at addressing the problem that the subjective selection of start prediction time (SPT) in rolling bearing remaining useful life (RUL) prediction will lead to excessive noise in the prediction signal, a linear-regression-based SPT point determination was proposed. The sliding window linear regression method was used to establish sliding windows in the root mean square (RMS) range to obtain the RMS gradient domain. The threshold for the RMS gradient was set, and the continuous trigger threshold mechanism to determine the SPT point was used. The experimental results show that the linear-regression-based method can adaptively determine the SPT point and improve the accuracy of life prediction.

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