Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN

Appana, Dileep Kumar; Islam, Rashedul; Kim, Jong‐Myon

doi:10.1007/978-3-319-51691-2_17

Cited by 18 publications

(11 citation statements)

References 11 publications

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“…To further verify the effectiveness of the proposed rolling-element bearing fault diagnosis methods, the performance of improved 2D LeNet-5 network and improved 1D LeNet-5 network are compared with that of the other nine different fault diagnosis methods based on machine learning or deep learning, including SVM [8], k-NN [9], K-Means [10], BPNN [11], compact 1D CNN without fine-tuning [27], AlexNet [23], VGG-19 [24], ResNet-50 [25] and traditional LeNet-5 network [31].…”

Section: Comparison With Other Fault Diagnosis Methodsmentioning

confidence: 99%

“…The machine learning method used in rolling-element bearing fault diagnosis firstly extracts fault features from vibration signals, and then maps the extracted fault features into the fault type of rolling-element bearing. The common machine learning methods for rolling-element bearing fault diagnosis include support vector machine (SVM) [8], k-nearest neighbor (k-NN) [9], K-Means for rotating machinery fault diagnosis, which has a high training speed and a strong transfer-learning ability.…”

Section: Introductionmentioning

confidence: 99%

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Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network is put forward. The following improvements to the traditional LeNet-5 network are made: the convolution and pooling layers are reasonably designed and the size and number of convolution kernels are carefully adjusted to improve fault classification capability; the batch normalization (BN) is adopted after each convolution layer to improve convergence speed; the dropout operation is performed after each full-connection layer except the last layer to enhance generalization ability. To further improve the efficiency and effectiveness of fault diagnosis, on the basis of improved 2D LeNet-5 network, an end-to-end rolling-element bearing fault diagnosis method based on the improved 1D LeNet-5 network is proposed, which can directly perform 1D convolution and pooling operations on raw vibration signals without any preprocessing. The results show that the improved 2D LeNet-5 network and improved 1D LeNet-5 network achieve a significant performance improvement than traditional LeNet-5 network, the improved 1D LeNet-5 network provides a higher fault diagnosis accuracy with a less training time in most cases, and the improved 2D LeNet-5 network performs better than improved 1D LeNet-5 network under small training samples and strong noise environment.Sensors 2020, 20, 1693 2 of 23 clustering [10], back propagation neural network (BPNN) [11], etc. The traditional rolling-element bearing fault diagnosis methods have been widely used, but with the increasing complexity of vibration signals, these methods have a certain limitation; however, the deep learning methods have a greater advantage in analyzing complicated and non-stationary vibration signals.The deep learning methods can automatically extract fault features from vibration signals [12], recently there are many researches are conducted on rolling-element bearing fault diagnosis using deep learning. Yin et al. [13] extracted the original features of vibration signals through time-domain analysis, frequency-domain analysis and wavelet transform, and obtained the low-dimensional features from 38 original features using the nonlinear global algorithm, and the low-dimensional features array is input into the deep belief network (DBN) to evaluate the performance status of rolling-element bearing. Liu et al. [14] obtained the spectrogram of vibration signals through STFT, used the stacked sparse auto-encoder (SAE) to automatically extract fault features, and employed the softmax regression to identify the fault type of rolling-element bearing. Liu et al. [15] used the recurrent neural network (RNN) to classify the faults of rolling-element bearing, and adopted the gated recurrent unit based denoising auto-encoder to enhance fault classification accuracy. Among different deep learning methods, compared with DBN, ...

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Section: Comparison With Other Fault Diagnosis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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“…The data-driven intelligent fault diagnosis methods based on machine learning or deep learning can fully dig the underlying fault feature information from the massive and complex vibration signals of rolling bearing, thus they are suitable for complex fault diagnosis of rolling bearing. In recent years, more and more researches have focused on the data-driven rolling bearing fault diagnosis, such as random forest (RF) [2], k-nearest neighbor [3], support vector machine [4], back VOLUME 4, 2016 propagation neural network [5], improved LeNet-5 network [6], deep convolutional neural network [7]- [9], deep recurrent neural network [10], deep residual learning [11], deep auto-encoder [12], and stacked sparse auto-encoder [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm

Wan

Zhang

et al. 2021

IEEE Access

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K-Means clustering algorithm is a typical unsupervised learning method, and it has been widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering algorithm is difficult to efficiently and accurately perform clustering analysis on the massive running-state monitoring data of rolling bearing. Therefore, a novel fault diagnosis method of rolling bearing using Spark-based parallel ant colony optimization (ACO)-K-Means clustering algorithm is proposed. Firstly, a Spark-based three-layer wavelet packet decomposition approach is developed to efficiently preprocess the running-state monitoring data to obtain eigenvectors, which are stored in Hadoop Distributed File System (HDFS) and served as the input of ACO-K-Means clustering algorithm. Secondly, ACO-K-Means clustering algorithm suitable for rolling bearing fault diagnosis is proposed to improve the diagnosis accuracy. ACO algorithm is adopted to obtain the global optimal initial clustering centers of K-Means from all eigenvectors, and the K-Means clustering algorithm based on weighted Euclidean distance is used to perform clustering analysis on all eigenvectors to obtain a rolling bearing fault diagnosis model. Thirdly, the efficient parallelization of ACO-K-Means clustering algorithm is implemented on a Spark platform, which can make full use of the computing resources of a cluster to efficiently process large-scale rolling bearing datasets in parallel. Extensive experiments are conducted to verify the effectiveness of the proposed fault diagnosis method. Experimental results show that the proposed method can not only achieve good fault diagnosis accuracy but also provide high model training efficiency and fault diagnosis efficiency in a big data environment.

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“…The time-frequency analyses adopt a machine learning method for diagnosing rolling bearings' faults which extracts fault characteristics from collected rolling bearing vibration signals and then, divide the features into several category. Commonly used machine learning methods for the failure recognition are support vector machine (SVM) [12], k-nearest neighbor (KNN) [13], and K-means clustering Continuous wavelet transform overcomes the shortcomings of the traditional method using Fourier transform. Thus, Wang et al [14] designed a support vector machine (SVM) classifier based on vibration signal analysis;…”

Section: Introductionmentioning

confidence: 99%

An Improved of Fault Diagnosis Using 1D-Convolutional Neural Network Model

Chen¹,

Liu²,

Yang³

et al. 2020

Preprint

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The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

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Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN

Cited by 18 publications

References 11 publications

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

A Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm

An Improved of Fault Diagnosis Using 1D-Convolutional Neural Network Model

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