scholarly journals Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

Entropy ◽  
2014 ◽  
Vol 16 (10) ◽  
pp. 5400-5415 ◽  
Author(s):  
Bin Zhang ◽  
Lijun Zhang ◽  
Jinwu Xu ◽  
Pingfeng Wang
Keyword(s):  
Performance Degradation ◽  
Rolling Element Bearings ◽  
Rolling Element

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%.

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.

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 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.

Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression

2017 ◽  
Vol 232 (6) ◽  
pp. 1118-1132 ◽  
Author(s):  
Akhand Rai ◽  
Sanjay H Upadhyay
Keyword(s):  
Support Vector Regression ◽  
Performance Degradation ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Health Index ◽  
Rolling Element ◽  
Useful Life ◽  
Self Organising Map ◽  
Quantisation Error

Rolling element bearings are critical components of rotating machines since the failure of rolling element bearings may cease the functioning of the entire equipment. The damages observed due to bearing failures are expeditious in nature and hence the need to develop an effective prognostic methodology becomes a requisite to prevent the sudden machinery breakdown. The performance degradation assessment and accurate determination of remaining useful life are the two key issues in prognostics of rolling element bearings. This paper proposes a degradation indicator based on self-organising map for the performance degradation assessment of bearings and later support vector regression is utilised to estimate the remaining useful life of bearings. The time-domain and frequency domain features extracted from the raw bearing vibration signals are supplied to the self-organising map classifier to achieve the degradation index termed as self-organising map-minimum quantisation error evolution in the paper. For estimating the remaining useful life of bearings, first the central trend of minimum quantisation error is extracted to achieve the feature vector defined as bearing health index in this work. The bearing health index is then used as input and the life percentage of the bearing is set to output in order to build the support vector regression prediction model for remaining useful life estimation of bearings. The proposed method is validated on the vibration signatures collected in a bearing test rig. The results show that the advocated method can efficiently track the evolution of deterioration and predict the remaining useful life of bearings.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

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