A Novel Fault Diagnosis Algorithm for Rolling Bearings Based on One-Dimensional Convolutional Neural Network and INPSO-SVM

Shao, Yang; Yuan, Xianfeng; Zhang, Chengjin; Song, Yong; Xu, Qingyang

doi:10.3390/app10124303

Cited by 21 publications

(20 citation statements)

References 35 publications

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“…After the one-dimensional signal is input into the network model, the features are automatically extracted layer by layer, and the extracted features' abstraction gradually becomes higher. Then the extracted features pass through the full connection layer and input layer, so as to realize the classification of different signals [12,22].…”

Section: One-dimensional Convolutional Neural Network (1dcnn)mentioning

confidence: 99%

“…As a popular tool in data-based fault diagnosis, the convolutional neural network has performed well in recent years benefits from its sparsity, shared weights, and other advantages. Yang proposed a hybrid fault diagnosis algorithm combining a one-dimensional convolutional neural network and support vector machine, which shows high precision in fault diagnosis of rolling bearing [12]. Li studied the application of CNN in fault diagnosis of aircraft hydraulic systems, the 1DMCCNN model proposed by Li realized the processing of one-dimensional time series signals and multi-sensor fusion [13].…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis Method for Aircraft EHA based on FCNN and MSPSO Hyperparameter Optimization

Li¹,

Li²,

Cao³

et al. 2022

Preprint

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Contrapose the highly integrated, multiple types of faults and complex working conditions of aircraft Electro Hydrostatic Actuator (EHA), to effectively identify its typical faults, we propose a fault diagnosis method based on the fusion convolutional neural networks (FCNN). First, the aircraft EHA fault data is encoded by GADF to obtain the fault feature images. Then we build an FCNN model that integrates the 1DCNN and 2DCNN, where the original 1D fault data is the input of the 1DCNN model, and the feature images obtained by GADF transformation are used as the input of 2DCNN. Multiple convolution and pooling operations are performed on each of these inputs to extract the features, next these feature vectors are spliced in the convergence layer, and the fully connected layers and the Softmax layers are finally used to attain the classification of aircraft EHA faults. Furthermore, the multi-strategy hybrid particle swarm optimization (MSPSO) algorithm is applied to optimize the FCNN to obtain a better combination of FCNN hyperparameters; MSPSO incorporates various strategies, including an initialization strategy based on homogenization and randomization, and an adaptive inertia weighting strategy, etc. The experimental result indicates that the FCNN model optimized by MSPSO achieves an accuracy of 96.86% for identifying typical faults of the aircraft EHA, respectively higher than the 1DCNN and the 2DCNN about 16.5% and 5.7%. Additionally, the FCNN model improved by MSPSO has a higher accuracy rate when compared to PSO.

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Section: One-dimensional Convolutional Neural Network (1dcnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Aircraft EHA based on FCNN and MSPSO Hyperparameter Optimization

Li¹,

Li²,

Cao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Other methods also utilize one-dimensional (1D) CNN models applied to raw monitoring signals in terms of simplicity and efficiency. For instance, Shao et al [39] proposed a hybrid model that combined 1D CNN and SVM with the support of an improved swarm optimization algorithm to enhance the performance and convergence speed. Zhou et al [40] proposed an 1D CNN-based fusing frequency feature matching algorithm to extract key frequency features in the signal spectrum for bearing fault diagnosis under noisy environments.…”

Section: Related Workmentioning

confidence: 99%

2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds

Pham

Kim

2021

Machines

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Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, methods for bearing fault diagnosis (FD) that can predict and categorize fault type, as well as the level of degradation, are increasingly necessary for factories. Owing to the advent of deep neural networks, especially convolutional neural networks (CNNs), intelligent FD methods have achieved significantly higher performance in terms of accuracy. However, in addition to accuracy, the efficiency issue still needs to be weathered in complicated diagnosis scenarios to adapt to real industrial environments. Here, we introduce a method based on multi-output classification, which utilizes the correlated features extracted for bearing compound fault type classification and crack-size classification to serve both aims. Additionally, the synergy of a time–frequency signal processing method and the proposed two-dimensional CNN helped the method perform well under the condition of variable rotational speeds. Monitoring signals of acoustic emission also had advantages for incipient FD. The experimental results indicated that utilizing correlated features in multi-output classification improved both the accuracy and efficiency of multi-task diagnosis compared to conventional CNN-based multiclass classification.

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“…A new tool wear state monitoring method, an entropybased bearing defect spareness detection method, a sparse map of the sensitive flter band of an axial piston pump, and an entropy-based bearing detection method are studied [5,6]. Numerical simulation and personalized diagnosis methods are used for detecting gear faults and the onedimensional convolutional neural network, and the rollingbearing fault diagnosis algorithm of INPSO-SVM have all been used in the rotating mechanical fault diagnosis neighborhood [7,8]. Numerical simulation and fnite element simulation is used to detect several fault diagnosis methods of bearings [9,10].…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Wavelet Transform-Based Gaussian Mixture Model for Bearing Fault Diagnosis

Cao

et al. 2023

Discrete Dynamics in Nature and Society

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The Gaussian mixture model (GMM) is an unsupervised clustering machine learning algorithm. This procedure involves the combination of multiple probability distributions to describe different sample spaces. Principally, the probability density function (PDF) plays a paramount role by being transformed into local linear regression to learn from unknown f failure samples, revealing the inherent properties and regularity of the data, and enhancing the subsequent identification of the operating status of the machine. The wavelet transform is a multiresolution transformation that can observe the signal gradually from coarse to fine, highlighting the localization analysis of nonstationary signals. Orthogonal wavelet transform selects the appropriate orthogonal wavelet function to transform so that the local characteristics of the signal in the time domain and frequency domain can be specifically described and the feature information of the original data can be mastered more effectively. In this study, a diagnostic method based on the Gaussian mixture model (OWTGMM) of orthogonal wavelet transform is proposed, in which orthogonal wavelet transform (OWT) is used to extract each detailed fault signal, the signal peak-to-peak value eigenvector is used as the construction model, and the GMM is used for fault classification. Based on the classification result from the rolling bearings’ test data, the use of detail signals extracted through OWT as the training data of the Gaussian mixture model promotes fast classification of bearing faults. Compared with the GMM without the extraction of the characteristic values, this method can reliably distinguish the categories of bearing faults about 100% of the time, which is consistent with the service life test chart. Furthermore, the unknown fault data is subject to classification with the orthogonal wavelet Gaussian model, and the bearing fault data is well distinguished, with an overall recognition rate of over 95%.

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A Novel Fault Diagnosis Algorithm for Rolling Bearings Based on One-Dimensional Convolutional Neural Network and INPSO-SVM

Cited by 21 publications

References 35 publications

Fault Diagnosis Method for Aircraft EHA based on FCNN and MSPSO Hyperparameter Optimization

Fault Diagnosis Method for Aircraft EHA based on FCNN and MSPSO Hyperparameter Optimization

2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds

Orthogonal Wavelet Transform-Based Gaussian Mixture Model for Bearing Fault Diagnosis

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