Supervised Classification Methods in Condition Monitoring of Rolling Element Bearings

2017 ◽  
pp. 133-145
Author(s):  
Paweł Różak ◽  
Jakub Zieliński ◽  
Piotr Czop ◽  
Adam Jabłoński ◽  
Tomasz Barszcz ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Supervised Classification ◽  
Rolling Element Bearings ◽  
Classification Methods ◽  
Rolling Element ◽  
Supervised Classification Methods

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.

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Nadège Dossat ◽  
Alain Mangé ◽  
Jérôme Solassol ◽  
William Jacot ◽  
Ludovic Lhermitte ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Supervised Classification ◽  
Protein Profiling ◽  
Clinical Proteomics ◽  
High Dimensional ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Supervised Classification Methods

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.

A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction

2014 ◽  
Vol 23 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Cagatay Catal
Keyword(s):  
Supervised Classification ◽  
Defect Prediction ◽  
Support Vector ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Data Set ◽  
Software Defect ◽  
Data Points ◽  
Supervised Classification Methods ◽  
Prediction Approach

AbstractPredicting the defect-prone modules when the previous defect labels of modules are limited is a challenging problem encountered in the software industry. Supervised classification approaches cannot build high-performance prediction models with few defect data, leading to the need for new methods, techniques, and tools. One solution is to combine labeled data points with unlabeled data points during learning phase. Semi-supervised classification methods use not only labeled data points but also unlabeled ones to improve the generalization capability. In this study, we evaluated four semi-supervised classification methods for semi-supervised defect prediction. Low-density separation (LDS), support vector machine (SVM), expectation-maximization (EM-SEMI), and class mass normalization (CMN) methods have been investigated on NASA data sets, which are CM1, KC1, KC2, and PC1. Experimental results showed that SVM and LDS algorithms outperform CMN and EM-SEMI algorithms. In addition, LDS algorithm performs much better than SVM when the data set is large. In this study, the LDS-based prediction approach is suggested for software defect prediction when there are limited fault data.

The Effect of Wear Evolution on Vibration-based Fault Detection in Tapered Roller Bearings

2019 ◽  
Vol 9 (1) ◽  
pp. 18-23
Author(s):  
K Rabeyee ◽  
X Tang ◽  
F Gu ◽  
A D Ball
Keyword(s):  
Condition Monitoring ◽  
Rolling Element Bearings ◽  
Rolling Bearings ◽  
Roller Bearings ◽  
Fault Type ◽  
Rolling Element ◽  
Tapered Roller ◽  
Magnitude Variation ◽  
Tapered Roller Bearings ◽  
The Impact

Rolling element bearings (REBs) are typical tribological components used widely in rotating machines. Their failure could cause catastrophic damage. Therefore, condition monitoring of bearings has always had great appeal for researchers. Usually, the detection and diagnostics of incipient bearing faults are achieved by characterising the weak periodic impacts induced by the collision of defective bearing components. However, race wear evolution, which is inevitable in bearing applications, can affect the contact between bearing elements and races, thereby decreasing the impact magnitudes and impeding detection performance. In this paper, the effect of wear evolution on the condition monitoring of rolling bearings is firstly analysed based on internal clearance changes resulting from the wear effect. Then, an experimental study is ingeniously designed to simulate wear evolution and evaluate its influence on wellknown envelope signatures according to measured vibrations from widely used tapered roller bearings. The fault type is diagnosed in terms of two indices: the magnitude variation of characteristic frequencies and the deviation of such frequencies. The experimental results indicate a signature decrease with regard to wear evolution, suggesting that accurate severity diagnosis needs to take into account both the wear conditions of the bearing and the signature magnitudes.

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