Induction Motor Bearing Fault Diagnosis Using Statistical
Among all fault types in induction motors, faults related to bearing failures occur in approximately 50% of the cases [].The key components of induction motor ball bearings are the ball, the outer raceway, and the inner raceway, along with the requirement that there must be uniform distance between the balls to avoid contact with each other, …
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Combined classification models for bearing fault diagnosis …
Deploys hybrid classifiers including LSTM and ANN for fine diagnosis of bearing faults. •. Proposes a novel algorithm termed as self improved SSA for fine tuning the weights of …
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Condition Monitoring of Roller Bearing by K-Star …
This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is …
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(PDF) A comparative study of naive Bayes classifier and …
A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal January 2015 International Journal of Decision Support Systems 1(1):115
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A classifier fusion system for bearing fault diagnosis
In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique ...
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Enhanced bearing fault detection using multichannel
In this study, we implemented and tested a new bearing fault diagnosis system based on the idea of utilizing multiple channels of sensor data simultaneously using a multi-channel 1D CNN architecture. The proposed classifier can process multiple axis vibration data simultaneously to achieve enhanced fault detection performance.
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"Dynamic Classifier Bearing System"
Abstract. The present invention refers to a bearing system for a vertically arranged drive axle (1) of a dynamic classifier, comprising bearings (2, 3) for axial and radial loads acting on the drive axle, and incorporating a housing (4) enclosing said bearings, which bearing housing incorporates an annular casing (4) supporting the inner envelope surface the …
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Comparison of Machine Learning Algorithms for Bearing
So, in this paper, we have employed different types of machine learning algorithms to predict four different bearing failures: (a) bearing health conditions (HC), (b) inner race …
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A Generic Intelligent Bearing Fault Diagnosis System …
Timely and accurate bearing fault detection and diagnosis is important for reliable and safe operation of industrial systems. In this study, performance of a generic real-time induction bearing fault diagnosis system employing compact adaptive 1D Convolutional Neural Network (CNN) classifier is extensively studied. In the literature, …
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Vibration-based fault diagnosis of dynamic rotating systems …
The main effects of system imbalance are detrimental damage to the load-bearing roller bearings and arches of the systems. The imbalance induces in the radial plane a vibration whose spectrum presents a component which the base frequency corresponds to the rotation frequency (fr). It represents the highest peak of low …
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A classifier fusion system for bearing fault diagnosis
ROC curves. Classifier fusion. 1. Introduction. Although the visual inspection of time- and frequency-domain features of measured signals is adequate for identifying machinery faults, there is a need for a reliable, fast and automated procedure of diagnosis ( Samanta et al., 2004 ). Due to the increasing demands for greater product quality and ...
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(PDF) Effective Time Domain Features for Identification of Bearing
BEARING FAULT USING LDA AND NB CLASSIFIERS . ... Sir MVIT, Bangalore, India . 2. School of Electrical Engineering, VIT University, Vellore, India ... While our system can achieve promising results ...
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Fault diagnosis of bearings through vibration signal …
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.
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Feature-based performance of SVM and KNN classifiers for …
Rolling element bearings (REBs) are vital parts of rotating machinery across various industries. For preventing breakdowns and damages during operation, it is crucial to establish appropriate techniques for condition monitoring and fault diagnostics of these bearings. The development of machine learning (ML) brings a new way of diagnosing …
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(PDF) Classifiers in Mising | Mark W. Post
However in the languages belonging to Eastern India, irrespective of the family, there is some sort of classifier system. Thus classifiers seem to be an areal feature in most of the Eastern and whole of the North-Eastern India. The purpose of the paper is to study if there is some semantic similarity among the classifier systems across language ...
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Oil Mist lubrication system
Oil mist lubrication is an advanced centralized lubrication solution comprised of the production and distribution of a continuous flow of atomized Oil particles.These particles are delivered directly to the bearing and metal surfaces, for a high quality, cost-effective lubrication solution. Oil mist is a mixture of clean, dry Air and Oil (Nebol), a lubricant that …
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Diagnosis and Classifications of Bearing Faults Using
A test rig setup (designed and manufactured by SKF India Ltd.) is used to obtain vibration signals for healthy and different types of faulty bearings. Test rig consists of 1 HP, three …
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A classifier fusion system for bearing fault diagnosis
A classifier fusion system for bearing fault diagnosis. December 2013. Expert Systems with Applications 40 (17):6788-6797. DOI: 10.1016/j.eswa.2013.06.033. Authors: Luana Batista. Banque Nationale ...
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Types of Classifiers in Mineral Processing
Rake Classifier. The Rake Classifier is designed for either open or closed circuit operation. It is made in two types, type "C" for light duty and type "D" for heavy duty. The mechanism and tank of …
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Influence of One-Way ANOVA and Kruskal–Wallis Based
The rotor-bearing-casing system is an important structural form in aero-engines, where the main shaft bearing plays an important role in the safe and efficient operation of the whole engine.
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Self-supervised learning-based dual-classifier domain …
To address this challenge, a self-supervised learning-based dual-classifier domain adaptation model (SLDDA) is presented for cross-domain fault diagnosis of bearings. Firstly, a dual-classifier classification determinacy metric is formulated to alleviate the output ambiguity between classifiers, which simultaneously considers the …
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A fault diagnosis of bearing through SVM classifier and data
In this paper, fault detection in the bearing of three-phase induction motor was performed by extracting the time domain features, wavelet energy features and wavelet entropy …
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Influence of One-Way ANOVA and Kruskal–Wallis Based …
fact that bearing defects occur in electric motors at a rate of 45% concedes the need of studying bearing fault diagnosis [9]. Researchers have done a lot of work on bearings and rotating machinery defect diagnosis. There have been many model-based methods [10], signal-based methods [11], and data-driven methods [12] proposed. To identify faults,
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Diagnosis of bearing fault in induction motor using Bayesian …
In the proposed system, accurate prediction of bearing condition is carried out using Bayesian optimization-based ensemble classifier (BOEC). The performance of the BOEC-based bearing fault diagnosis system is compared with other conventional techniques and the comparison results confirm the superior performance of the …
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A comparative study of naïve Bayes classifier and Bayes net classifier …
Bearing is an important and necessary part of any big or small machinery and for proper working of machinery the bearing condition should be good. Hence, there is a requirement for continuous bearing monitoring. For the condition monitoring of bearings sound signal can be used. This paper uses sound signal for condition monitoring of roller …
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Automatic Traffic Counter and Classifier (ATCC)
Automatic Traffic Counter and Classifier (ATCC) monitors the real-time traffic flow of a road section, keeps count of vehicles, and classify them according to their pre-defined classes. EFKON's Video-based …
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Comparison of Machine Learning Algorithms for Bearing
Develop approach based on convolutional neural networks, and a random forest classifier for bearing fault detection from the data itself that got results accuracy of 93.61% and the latter an accuracy of 87.25% . Proposed a new strategy-based support vector machines (SVMs) to reduce noise effect in bearing fault diagnosis systems.
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(PDF) A Comparative Study of SVM Classifiers and Artificial …
Hence these features are good features for cla ssifying the good and bad bearings using the SVM and ANN classifier. Sunil T yagi and S. K. Panigrahi, V ol. 3, No. 1, 2017
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Identification and diagnosis of concurrent faults in rotor-bearing …
An effective method for identifying and diagnosing the concurrent fault combined by two or more single faults is yet to be further developed because most existing approaches focus on single faults. On the other hand, rotor-bearing system is an important part of rotating machinery. Therefore a new fault identification and diagnosis method …
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(PDF) Feature-based performance of SVM and KNN …
The development of machine learning (ML) brings a new way of diagnosing the fault of rolling element bearings. In the current work, ML models, namely, Support Vector …
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