Journal ArticleDOI
Fault diagnosis of bearings through vibration signal using Bayes classifiers
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TLDR
This study concerns with fault diagnosis through machine learning approach of bearing using vibration signals of bearings in good and simulated faulty conditions, which discusses the effect of various parameters on classification accuracy.Abstract:
Bearings are an inevitable part in industrial machineries, which is subjected to wear and tear. Breakdown of such crucial components incur heavy losses. This study concerns with fault diagnosis through machine learning approach of bearing using vibration signals of bearings in good and simulated faulty conditions. The vibration data was acquired from bearings using accelerometer under different operating conditions. Vibration signals of a bearing contain the dynamic information about its operating condition. The descriptive statistical features were extracted from vibration signals and the important ones were selected using decision tree (dimensionality reduction). The decision tree has been formulated using J48 algorithm. The selected features were then used for classification using Bayes classifiers namely, Naive Bayes and Bayes net. The paper also discusses the effect of various parameters on classification accuracy.read more
Citations
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Fault diagnosis of rolling element bearings using basis pursuit
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Basic research on machinery fault diagnostics: Past, present, and future trends
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Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data
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A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions
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References
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Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Journal ArticleDOI
Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing
TL;DR: This paper illustrates the use of a Decision Tree that identifies the best features from a given set of samples for the purpose of classification using Proximal Support Vector Machine (PSVM), which has the capability to efficiently classify the faults using statistical features.
Journal ArticleDOI
Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition
TL;DR: In this paper, a study on the application of sound pressure and vibration signals to detect the presence of defects in a rolling element bearing using a statistical analysis method is presented, which is most suitable with random signals where other signal analysis methods based on the assumptions of deterministic signals are not applicable.
Journal ArticleDOI
Decision tree and PCA-based fault diagnosis of rotating machinery
Weixiang Sun,Jin Chen,Jiaqing Li +2 more
TL;DR: The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.
Journal ArticleDOI
A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing
TL;DR: In this article, an analytical model for predicting the effect of a localized defect on the ball bearing vibrations was presented, where the contacts between the ball and the races were considered as non-linear springs.
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