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

Wan, Lanjun; Chen, Yiwei; Li, Hongyang; Li, Changyun

doi:10.3390/s20061693

Cited by 59 publications

(37 citation statements)

References 43 publications

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“…In order to validate the feature extraction availability of the proposed model, the performance of the CWT-AlexNet network is compared with other commonly used models, including standard LeNet-5, AlexNet and improved 2D LeNet-5 [ 54 ] network. The detailed setting of improved 2D LeNet-5 network can be searched in [ 54 ]. After repeating experiments 10 times, the average test accuracy, standard deviation (Std), training time, and test time are taken as evaluation indicators.…”

Section: Experimental Verificationmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

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Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

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Section: Experimental Verificationmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

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“…Step 4: Update the initial clustering centers. k initial clustering centers are recalculated by (6), which are regarded as the new initial clustering centers.…”

Section: ) Aco-k-means Clustering Algorithmmentioning

confidence: 99%

“…, E s * m/n >} of pathRDD, all eigenvectors of the s-th RDD partition are divided into k groups according to different access points, and the average valueĒ sj of all eigenvectors in the j-th group is calculated by (6), where 1 ≤ s ≤ n and 1 ≤ j ≤ k. k average values of each RDD partition of pathRDD are gathered from each worker node to the master node, and µ j = 1 n n s=1Ē sj (1 ≤ j ≤ k) is used as the j-th new initial clustering center, and k updated initial clustering centers are broadcasted from the master node to each worker node.…”

Section: Proposed Spark-based Parallel Aco-k-means Clustering Algomentioning

confidence: 99%

“…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%

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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 first classic architecture of CNN was the network proposed by Yann LeCun called LeNet. Even though it has existed for many years, it is still widely used in fault diagnosis and gas identification [ 16 , 17 ]. The more complicated network proposed by Alex Krizhevsky called AlexNet appeared later, but it does not differ much from LeNet in architecture [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Chen

Lei

2020

Sensors

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This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.

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

Cited by 59 publications

References 43 publications

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

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

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

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