An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network

Wang, Huaqing; Shi, Li; Song, Liuyang; Cui, Lingli; Wang, Pengxin

doi:10.1109/tim.2019.2928346

Cited by 141 publications

(46 citation statements)

References 37 publications

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“…Numerous numbers of DNN models have been introduced and achieved great success in a vast number of applications. Basically, all DNN models can be considered to be the variants of four basis NNs: autoencoder [16], restricted Boltzmann machine [17], recurrent neural network [18], and convolutional neural network (CNN) [19]. Among those NNs, CNN-like models are the most popular in intelligent signal-based fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

Hoang

Tran

Van

2021

Sensors

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This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.

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Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

Hoang

Tran

Van

2021

Sensors

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“…It is widely used in pattern recognition, fault diagnosis, automatic control, and other fields. 4,5 However, the defects of neural network, such as long training time, poor generalization ability, and slow convergence speed, are not conducive to the pattern recognition of small samples.…”

Section: Introductionmentioning

confidence: 99%

A rolling bearing fault diagnosis method based on LSSVM

Gao

Wei

et al. 2020

Advances in Mechanical Engineering

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The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

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“…Combining data from different sensors has been done successfully for numerous applications [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. In this work, we employ a Bayesian framework and machine learning methods to build a model that combines radar batted ball data and optical running speed data.…”

Section: Introductionmentioning

confidence: 99%

Combining Radar and Optical Sensor Data to Measure Player Value in Baseball

Healey

2020

Sensors

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Evaluating a player’s talent level based on batted balls is one of the most important and difficult tasks facing baseball analysts. An array of sensors has been installed in Major League Baseball stadiums that capture seven terabytes of data during each game. These data increase interest among spectators, but also can be used to quantify the performances of players on the field. The weighted on base average cube model has been used to generate reliable estimates of batter performance using measured batted-ball parameters, but research has shown that running speed is also a determinant of batted-ball performance. In this work, we used machine learning methods to combine a three-dimensional batted-ball vector measured by Doppler radar with running speed measurements generated by stereoscopic optical sensors. We show that this process leads to an improved model for the batted-ball performances of players.

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An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network

Cited by 141 publications

References 37 publications

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

A rolling bearing fault diagnosis method based on LSSVM

Combining Radar and Optical Sensor Data to Measure Player Value in Baseball

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