Cross-fuzzy entropy-based approach for performance degradation assessment of rolling element bearings

2017 ◽  
Vol 232 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Keheng Zhu ◽  
Xigeng Song
Keyword(s):  
Life Time ◽  
Degradation Process ◽  
Performance Degradation ◽  
Fuzzy Entropy ◽  
Rolling Element Bearings ◽  
Global Trends ◽  
Global Trend ◽  
Rolling Element ◽  
Bearing Vibration ◽  
Made In

Exploring effective indicators is significant for the assessment of the bearing performance degradation, which is crucial to realize the condition-based maintenance. In this paper, the cross-fuzzy entropy is introduced and is used to measure the similarity of patterns between normal signals and tested signals of the rolling element bearings, and the degree of similarity is used as an indicator of the bearing performance degradation. The original cross-fuzzy entropy focuses on the local characteristics of the signal and neglects its global trend. However, the global characteristics and global trends of bearing vibration signals may vary as the bearings degrade gradually. Therefore, a change has been made in the implementation of the original cross-fuzzy entropy algorithm to overcome this limitation and the modified cross-fuzzy entropy is more suitable for reflecting the whole degradation process of rolling element bearings. The experimental results demonstrate that the modified cross-fuzzy entropy can assess the bearing performance degradation process over their whole life time clearly and effectively.

scholarly journals Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

2017 ◽  
Vol 17 (5) ◽  
pp. 219-225 ◽  
Author(s):  
Keheng Zhu ◽  
Xiaohui Jiang ◽  
Liang Chen ◽  
Haolin Li
Keyword(s):  
Degradation Process ◽  
Performance Degradation ◽  
Fuzzy Entropy ◽  
Rolling Element Bearings ◽  
Vibration Data ◽  
Global Trend ◽  
Rolling Element ◽  
Assessment Approach ◽  
Robustness To Noise ◽  
Degradation Evaluation

Abstract Rolling element bearings are an important unit in the rotating machines, and their performance degradation assessment is the basis of condition-based maintenance. Targeting the non-linear dynamic characteristics of faulty signals of rolling element bearings, a bearing performance degradation assessment approach based on improved fuzzy entropy (FuzzyEn) is proposed in this paper. FuzzyEn has less dependence on data length and achieves more freedom of parameter selection and more robustness to noise. However, it neglects the global trend of the signal when calculating similarity degree of two vectors, and thus cannot reflect the running state of the rolling element bearings accurately. Based on this consideration, the algorithm of FuzzyEn is improved in this paper and the improved FuzzyEn is utilized as an indicator for bearing performance degradation evaluation. The vibration data from run-to-failure test of rolling element bearings are used to validate the proposed method. The experimental results demonstrate that, compared with the traditional kurtosis and root mean square, the proposed method can detect the incipient fault in advance and can reflect the whole performance degradation process more clearly.

Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance

2017 ◽  
Vol 24 (14) ◽  
pp. 3194-3205 ◽  
Author(s):  
Keheng Zhu
Keyword(s):  
Degradation Process ◽  
Performance Degradation ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Multi Scale ◽  
Nonlinear Dynamic Characteristics ◽  
Rolling Element ◽  
Bearing Vibration ◽  
Degradation Indicator

Performance degradation assessment is crucial to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new method for performance degradation assessment of rolling element bearings is proposed based on hierarchical entropy (HE) and general distance. First, considering the nonlinear dynamic characteristics of bearing vibration signals, the HE method is utilized to extract feature vectors, which can obtain more bearing state information hidden in the vibration signals than sample entropy (SampEn) and multi-scale entropy (MSE). Then, the general distance between the feature vectors of the normal data and those of the tested data is designed as a degradation indicator by combining Euclidean distance and cosine angle distance. The experimental results indicate that this indicator can detect the incipient defects well and can effectively reflect the whole degradation process of rolling element bearings. Moreover, the designed indicator has some advantages over kurtosis and root mean square (RMS) values.

scholarly journals Chaotic prediction of vibration performance degradation trend of rolling element bearing based on Weibull distribution

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041989219
Author(s):  
Li Cheng ◽  
Xintao Xia ◽  
Liang Ye
Keyword(s):  
Weibull Distribution ◽  
Relative Error ◽  
High Reliability ◽  
Prediction Method ◽  
Performance Degradation ◽  
Rolling Element Bearing ◽  
Maximum Relative Error ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Vibration Performance

Rolling element bearings are used in all rotating machinery, and the degradation performance of rolling element bearings directly affects the performance of the machine. Therefore, high reliability prediction of the performance degradation trend of rolling element bearings has become an urgent research problem. However, the degradation characteristics of the rolling element bearings vibration time series are difficult to extract, and the mechanism of performance degradation is very complicated. The accurate physical model is difficult to establish. In view of the above reasons, based on the vibration performance data of rolling element bearings, a model of bearing performance degradation trend parameter based on wavelet denoising and Weibull distribution is established. Then, the phase space reconstruction of the series of bearing performance degradation trend parameter is carried out, and the prognosis is obtained by the improved adding weighted first-order local prediction method. The experimental results show that the bearing vibration performance degradation parameter can accurately depict the degradation trend of the bearing, and the reliability level is 91.55%; and the prediction of bearing performance degradation trend parameter is satisfactory: the mean relative error is only 0.0053% and the maximum relative error is less than 0.03%.

Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine

2019 ◽  
Vol 41 (14) ◽  
pp. 4013-4022 ◽  
Author(s):  
Keheng Zhu ◽  
Liang Chen ◽  
Xiong Hu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

Multi-scale fuzzy entropy (MFE) is a recently developed non-linear dynamic parameter for measuring the complexity of vibration signals of rolling element bearing over different scales. However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence’s global characteristics while the bearing vibration signals’ global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.

Bearing Signature Analysis as a Medium for Fault Detection: A Review

2007 ◽  
Vol 130 (1) ◽  
Author(s):  
M. S. Patil ◽  
Jose Mathew ◽  
P. K. RajendraKumar
Keyword(s):  
Fault Detection ◽  
Vibration Measurement ◽  
Signature Analysis ◽  
Rolling Element Bearings ◽  
Bearing Defects ◽  
Rolling Element ◽  
Vibration Signature ◽  
Recent Trends ◽  
Bearing Vibration ◽  
Detection And Diagnosis

Rolling element bearings find widespread domestic and industrial application. Defects in bearing unless detected in time may lead to malfunctioning of the machinery. Different methods are used for detection and diagnosis of the bearing defects. This paper is intended as a tutorial overview of bearing vibration signature analysis as a medium for fault detection. An explanation for the causes for the defects is discussed. Vibration measurement in both time domain and frequency domain is presented. Recent trends in research on the detection of the defects in bearings have been included.

scholarly journals Influence of raceway waviness on the level of vibration in rolling-element bearings

2017 ◽  
Vol 65 (4) ◽  
pp. 541-551 ◽  
Author(s):  
S. Adamczak ◽  
P. Zmarzły
Keyword(s):  
Ball Bearing ◽  
Correlation Coefficients ◽  
Rolling Element Bearings ◽  
Test Results ◽  
Frequency Bandwidth ◽  
Surface Waviness ◽  
Medium Frequency ◽  
Method Of Calculation ◽  
Rolling Element ◽  
Bearing Vibration

AbstractThis paper provides a quantitative analysis of how raceway waviness (RONt) in 6304-type bearings affects their vibration. The waviness of bearing races was measured at the actual points of contact between the balls and the races. The measurements were conducted in the range of 16–50 undulations per revolution (UPR). The bearing vibration was analyzed in three bandwidths of frequency: low (LB) (50 ÷ 300 Hz), medium MB (300 ÷ 1800 Hz) and high HB (1800 ÷ 10 000 Hz), as well as in the full RMS bandwidth. The paper also presents the procedure used to determine the actual points of contact between the ball and each race to specify the point of waviness measurement. The method of calculation of the contact angle for a ball bearing is also discussed. The Pearson linear correlation coefficients were determined to analyze the relationships between the waviness parameters and the level of vibration. The test results show that an increase in the surface waviness on the inner and outer raceways causes an increase in the vibration level. The influence is most visible for the medium frequency bandwidth.

Paper 11: Experiments in the Aerosol Lubrication of Power Gearing

1966 ◽  
Vol 181 (15) ◽  
pp. 184-191
Author(s):  
P. G. Howell
Keyword(s):  
Power Transmission ◽  
No Reduction ◽  
Cooling Capacity ◽  
Bulk Temperature ◽  
Rolling Element Bearings ◽  
Rolling Mills ◽  
Rolling Element ◽  
Nitrided Case ◽  
Tooth Surfaces ◽  
Made In

Aerosol lubrication has been most fully exploited in the field of rolling element bearings, notably for rolling mills but also in other machinery. The paper describes the application of an aerosol system to a power-transmission gearbox. The aerosol equipment was fitted to one gearbox of a 5-in-centres back-to-back rig so that either conventional sprays or the aerosol could be used. Involute test gears were made in through-hardened and nitrided case-hardened steels. Under aerosol lubrication the following points were noted: (1) because of the lower cooling capacity of the aerosol, operating temperatures were much higher than under spray lubrication; (2) the through-hardened gears failed by scuffing when their bulk temperature reached 90°C; (3) for the same temperature rise the nitrided gears transmitted 2·3 times the power, and operated at higher temperatures and powers without damage; and (4) no reduction could be detected in losses, suggesting that the losses attributable to churning of excess sprayed lubricant are balanced by an increase in tooth friction with aerosol. The experiments showed that power transmission gearing can be successfully lubricated by an aerosol, especially if the tooth surfaces are protected by nitriding.

A general anomaly detection approach applied to rolling element bearings via reduced-dimensionality transition matrix analysis

2015 ◽  
Vol 230 (13) ◽  
pp. 2169-2180 ◽  
Author(s):  
Bradley W Harris ◽  
Michael W Milo ◽  
Michael J Roan
Keyword(s):  
System Modeling ◽  
Ground Truth ◽  
Matrix Analysis ◽  
Rolling Element Bearings ◽  
Frequency Method ◽  
Time Frequency ◽  
Vibration Data ◽  
Machine Failure ◽  
Rolling Element ◽  
Bearing Vibration

Rolling element bearings are vital components in most rotating machines. Bearings often operate in harsh environments where manufacturing imperfections, misalignments, and fatigue can result in reduced component lifespan. These failures are often preceded by changes in the normal vibration of the system. Modeling and detecting these vibrational anomalies is common practice in predicting machine failure. This paper develops and implements a novel approach to detecting bearing vibration anomalies in the time–frequency domain. The performance of the new approach is quantified using both simulated and experimental bearing vibration data. In these ground-truth experiments, the proposed time–frequency method successfully detects anomalies (>98% true positive) using short time spans (<0.1 s) with low false alarm rates (<1% false positive). Using experimental data, this time–frequency approach is shown to outperform one-dimensional time series analysis techniques.

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