scholarly journals Distance and Density Similarity Based Enhancedk-NN Classifier for Improving Fault Diagnosis Performance of Bearings

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Sharif Uddin ◽  
Md. Rashedul Islam ◽  
Sheraz Ali Khan ◽  
Jaeyoung Kim ◽  
Jong-Myon Kim ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Similarity Measure ◽  
Classification Accuracy ◽  
Diagnostic Performance ◽  
Nearest Neighbor ◽  
Neighborhood Size ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ae Signals

An enhancedk-nearest neighbor (k-NN) classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditionalk-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size,k. This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposedk-NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE) signals. Experimental results demonstrate that the proposed scheme, which uses the enhancedk-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size,k.

scholarly journals Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

2020 ◽  
Vol 10 (20) ◽  
pp. 7068
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Extraction Methods ◽  
High Accuracy ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

On the Use of Spectral Averaging of Acoustic Emission Signals for Bearing Fault Diagnostics

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Brandon Van Hecke ◽  
David He ◽  
Yongzhi Qu
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Reduction Technique ◽  
Signal Frequency ◽  
Fault Diagnostics ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Area Of Interest ◽  
Ae Signals ◽  
Spectral Averaging

For years, vibration analysis has been the industry standard for bearing fault diagnosis. However, due to the various advantages over vibration based techniques, the quantification of acoustic emission (AE) for bearing health diagnosis has been an area of interest for recent years. Additionally, most AE based methodologies to date utilize data mining technologies. Presented in this paper is a new approach, combining a heterodyne based frequency reduction technique, time synchronous resampling, and spectral averaging to process AE signals and compute condition indicators (CIs) for bearing fault diagnostics. First, the heterodyne based frequency reduction technique allows the AE signal frequency to be down shifted from several MHz to less than 50 kHz, which approaches that of vibration based methodologies. Next, the sampled AE signals are band pass filtered to retain the useful information related to the bearing defects. Last, a trigger signal is utilized to time synchronously resample the AE signals to allow the calculation of a spectral average and the extraction and evaluation of CIs for bearing fault diagnosis. The technique presented in this paper is validated using the AE signals of seeded fault steel bearings on a bearing test rig. Presented is an effective AE based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage. Moreover, the effectiveness of the presented approach is established through the comparison of both AE and vibration data.

Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropy

2016 ◽  
Vol 231 (17) ◽  
pp. 3228-3237 ◽  
Author(s):  
Xueli An ◽  
Luoping Pan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Nearest Neighbor ◽  
Roller Bearing ◽  
Approximate Entropy ◽  
Vibration Signals ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Iterative Filtering

For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.

scholarly journals Enhanced DET-Based Fault Signature Analysis for Reliable Diagnosis of Single and Multiple-Combined Bearing Defects

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
In-Kyu Jeong ◽  
Myeongsu Kang ◽  
Jaeyoung Kim ◽  
Jong-Myon Kim ◽  
Jeong-Min Ha ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Roller Bearing ◽  
Signature Analysis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Average Classification Accuracy ◽  
Bearing Defects ◽  
Bearing Failures ◽  
Cylindrical Roller Bearing

To early identify cylindrical roller bearing failures, this paper proposes a comprehensive bearing fault diagnosis method, which consists of spectral kurtosis analysis for finding the most informative subband signal well representing abnormal symptoms about the bearing failures, fault signature calculation using this subband signal, enhanced distance evaluation technique- (EDET-) based fault signature analysis that outputs the most discriminative fault features for accurate diagnosis, and identification of various single and multiple-combined cylindrical roller bearing defects using the simplified fuzzy adaptive resonance map (SFAM). The proposed comprehensive bearing fault diagnosis methodology is effective for accurate bearing fault diagnosis, yielding an average classification accuracy of 90.35%. In this paper, the proposed EDET specifically addresses shortcomings in the conventional distance evaluation technique (DET) by accurately estimating the sensitivity of each fault signature for each class. To verify the efficacy of the EDET-based fault signature analysis for accurate diagnosis, a diagnostic performance comparison is carried between the proposed EDET and the conventional DET in terms of average classification accuracy. In fact, the proposed EDET achieves up to 106.85% performance improvement over the conventional DET in average classification accuracy.

scholarly journals Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shilun Zuo ◽  
Zhiqiang Liao
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Canonical Discriminant Analysis ◽  
Selection Scheme ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Parameters Selection ◽  
Fault Features ◽  
Diagnosis Model

Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model. Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result. For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal. The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm. The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters. The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result. The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.

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