Degradation State Recognition of Rolling Bearing Based on K‐Means and CNN Algorithm

Zhou, Qicai; Shen, Hehong; Zhao, Jiong; Liu, Xingchen; Xiong, Xiaolei

doi:10.1155/2019/8471732

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Em Zhou et al (2019)é utilizada uma Rede Neural Convolucional (RNC) para classificação do estado de degradação dos rolamentos. O procedimento consistiu de duas etapas.…”

Section: Revisão Bibliográficaunclassified

Aplicação de Machine Learning e Descritores Estatísticos para Diagnóstico de Falhas em Rolamentos de Motores Elétricos Industriais

Soares

Pinto

Lobão

2020

Anais Do Congresso Brasileiro De Automática 2020

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Esse artigo faz uma análise do desempenho dos classificadores k-Nearest Neighbour, Support Vector Machine, Decision Tree e Random Forest, para a classificação de sinais de vibração adquiridos a partir de rolamentos normais e de rolamentos nos quais foram introduzidas falhas artificiais. Para a extração de descritores foram consideradas duas abordagens. A primeira utiliza Análise de Componente Principal (PCA) para extração das componentes principais dos sinais originais, que são utilizadas como descritores para a modelagem. A segunda obtém os modelos a partir de descritores estatísticos dos sinais no domínio do tempo. O conjunto de dados utilizados nos testes é o CWRU Bearing Dataset, e os resultados mostram que os descritores estatísticos apresentam boa separabilidade das classes favorecendo o desempenho dos modelos de classificação, que obtém taxas superiores a 99% de acurácia nos classificadores Árvore de Decisão e Floresta Aleatória.

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“…Em Zhou et al (2019)é utilizada uma Rede Neural Convolucional (RNC) para classificação do estado de degradação dos rolamentos. O procedimento consistiu de duas etapas.…”

Section: Revisão Bibliográficaunclassified

Aplicação de Machine Learning e Descritores Estatísticos para Diagnóstico de Falhas em Rolamentos de Motores Elétricos Industriais

Soares

Pinto

Lobão

2020

Anais Do Congresso Brasileiro De Automática 2020

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“…The x-axis denotes the Euclidean distance between each signal value while the y-axis indicates the stress wave signals represented by the corresponding labels (see Table II). The linkage 'centroid' is often used due its convenient computation to represent the center of the shape predicted using AR coefficients [17], [18].…”

Section: B Pattern Recognitionmentioning

confidence: 99%

The effect of linkages in the Hierarchical Clustering of Auto-Regressive algorithm for Defect Identification in Heat Exchanger Tubes

Halim*,

Jamaludin,

Putra

2019

IJRTE

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Pattern recognition approach based on Auto-Regressive (AR) algorithm is an alternative way to provide a more accurate defect identification from stress wave propagated along ASTM A179 heat exchanger tubes. The AR algorithm characterizes the shape of the stress wave signals by AR coefficients and clustered using ‘centroid’ linkages. However, the increase of number of stress waves limiting the function of clustering into meaningful groups. This paper proposes the ‘ward’ linkages as an improved hierarchical clustering method to define the defect features from the reference tube signals and those from the artificially induced defective tubes. The clustering results from the ‘ward’ linkages were represented via a dendrogram showing the hidden pattern between clusters. The defect in the heat exchanger tubes are easily interpreted from the dendrogram and can be successfully identified from Maximum Group Distance Criteria (MGDC). The pattern recognition approach using ‘ward’ linkages in AR algorithm has been shown to effectively identify the defects in the heat exchanger tubes

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“…Furthermore, Zhou et al presented a study in whichthe K-mean algorithm was used to label the un-labelled signals [13]. A comparison of the performance between ANN and KNN was carried out by Gunerkar et al [14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps

Tayyab

Chatterton

Pennacchi

2022

Sensors

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Vibration analysis is an established method for fault detection and diagnosis of rolling element bearings. However, it is an expert oriented exercise. To relieve the experts, use of Artificial Intelligence (AI) techniques such as deep neural networks, especially convolutional neural networks (CNN) have gained much attention of the researchers because of their image classification and recognition capability. Most of the researchers convert the vibration signal into representative time frequency vibration images such as spectrogram, and scalogram. These images are used as input to train the CNN model for fault diagnosis. Commonly, fault diagnosis is performed under same operating conditions, where models are trained and deployed for prediction under same operating conditions. However, outside laboratory environment, in real world applications, different operating conditions, such as variable speed, may be encountered. With the change in speed, characteristic frequencies of vibration signal will also change, which will result in changing the vibration image. Consequently, performance of CNN model may drop significantly for prediction under different operating conditions. To get the training data from all potential operating conditions may not be feasible for most of the real-world applications. Therefore, there is need to find some signal properties which are invariant to change in operating conditions and only change due to change in health state. So that models trained under one set of operating conditions may predict correctly under different operating conditions. This paper proposes a defect diagnosis method for rolling element bearings, under variable operating conditions (speed and load) based on CNN and order maps. These maps exhibit consistent properties under varying speed; therefore, they can be used to train the CNN model for fault diagnosis under variable speed. Effect of load change on these order maps is experimentally studied and it is found that proposed method can do fault diagnosis of rolling element bearings under variable speed and load with high accuracy.

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Degradation State Recognition of Rolling Bearing Based on K‐Means and CNN Algorithm

Cited by 8 publications

References 12 publications

Aplicação de Machine Learning e Descritores Estatísticos para Diagnóstico de Falhas em Rolamentos de Motores Elétricos Industriais

Aplicação de Machine Learning e Descritores Estatísticos para Diagnóstico de Falhas em Rolamentos de Motores Elétricos Industriais

The effect of linkages in the Hierarchical Clustering of Auto-Regressive algorithm for Defect Identification in Heat Exchanger Tubes

Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps

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