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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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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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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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This paper aims to identify and classify the faults present in the rotor-bearing system using KNN based on extracted bearing characteristic frequency sets. The studied work …
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Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults Mohd Atif Jamil1, Md Asif Ali Khan2, Sidra Khanam3 Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India author E-mail: 1atif.mechtech@gmail, 2asifalikha28@gmail, [email protected] …
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The research paper presents a comparative study of artificial neural network (ANN) and support vector machine (SVM) using continuous wavelet transforms and energy entropy approaches for fault diagnosis and classification of rolling element bearings. An experimental test rig is used to acquire the vibration signals of healthy and faulty …
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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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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 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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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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The dataset of healthy and faulty bearings is collected using a four-channel Fast Fourier Transform Analyzer (FFT) analyzer. However, the statistical feature extraction technique has been used to evaluate the accuracy and performance of artificial neural network, support vector machine, logistic regression, and decision tree algorithms based …
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The Oil-based analysis is limited to bearing systems which have an oil supply system, while temperature-based analysis can be unreliable towards the end of the bearing's life cycle due to a substantial increase in the system temperature at the end of the cycle, creating false alarms (Heng, et al., 2009).
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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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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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Using open-source Case Western Reserve University bearing data, machine learning classifiers are trained with extracted time-domain and frequency- domain features and show that frequency-domain features are more convincing for the training of ML models, and the KNN classifier has a high level of accuracy compared to SVM. Rolling …
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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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F EATURE-BASED PERFORMANCE OF SVM AND KNN CLASSIFIERS FOR DIAGNOSIS OF ROLL ING ELEMENT BEARING FAULTS. M OHD A TIF JAMIL, M D A SIF A LI K HAN, S IDRA K HANAM ISSN P RINT 2345 -0533, ISSN O NLINE 2538 -8479, K AUNAS, L ITHUANIA 37 the accuracy criterion of fault diagnosis is more helpful in selecting fault …
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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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The goal and the objective of this project is to predict the suspect of criminal and its location by analyzing criminal records and finding criminal hotspots. Investigations in India regarding larceny are very futile in nature and seldom result in apprehension of the criminal. Our software is for police officials to lead their investigation effectively and work towards …
DetailsBlind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise. Despite BD's desirable feature in adaptability and mathematical interpretability, a significant challenge persists: How to effectively integrate BD with fault-diagnosing …
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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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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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The Acro-set system is used to facilitate bearing assembly and optimize bearing settings and performance. After the bearings were installed and positioned, the classifier was returned to the plant ...
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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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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 …
DetailsIn this study, the use of 1D CNN is proposed in intelligent bearing fault diagnosis system. The 1D CNN architecture is illustrated in Fig. 1. The 1D raw vibration data are preprocessed by filtering, decimation and normalization before being input to the adaptive 1D CNN classifier for testing. Then, there are three convolution layers with filter.
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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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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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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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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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India. a) Corresponding Author: vimalvaniya.researchscholar@gmail. ... a comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors," ... vibration-based fault diagnosis of a rotor bearing system using artificial neural network and support vector machine," int. J. Model. Identif.
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The convolutional neural network (CNN) was utilized most widely in extracting representative features of bearing faults. Fundamental to this, the hybrid models based on the CNN and individual classifiers were proposed to diagnose bearing faults. However, CNN may not be suitable for all bearing fault classifiers.
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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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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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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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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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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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The statistical parameter data are used as the input data for the classifiers to train the system to classify the fault. References [1] P.K. Kankar, S.C. Sharma, S.P. Harsha, Fault diagnosis of ball bearings using continuous wavelet transform, Appl. Soft Computing. 11 (2011) 2300--2312. ... Navin Kumar, Motor Current Signature Analysis for ...
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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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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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