Fault Diagnosis for Machinery based on Feature Selection and Probabilistic Neural Network

Li, Haiping

doi:10.23940/ijpe.17.07.p20.11651170

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…As can be seen, several fields might benefit from the use of feature selection ensembles for preprocessing purposes, since they usually improve accuracy, while boosting stability and reducing the computational costs of pattern recognition. The areas mentioned above have covered some of the more popular applications for feature selection, but the literature describes many more application areas, as diverse as intrusion detection [132,133,134,135,136], machinery fault diagnosis, [137,138,139,140,141,142,143], or automatic evaluation of open response assignments [144].…”

Section: Fields Of Applicationmentioning

confidence: 99%

Ensembles for feature selection: A review and future trends

2019

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Section: Fields Of Applicationmentioning

confidence: 99%

Ensembles for feature selection: A review and future trends

2019

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“…Deng in the literature [20] generalized the Shannon entropy and applied it to the uncertainty measure of evidence. Its defined Deng entropy is represented by equation (10).…”

Section: Deng Entropymentioning

confidence: 99%

“…Therefore, how to integrate uncertain information and obtain accurate and effective decision-making results has become the focus of research. As multi-sensor information fusion system is developing rapidly, decision fusion of target recognition has gradually become a hot topic for experts and scholars at home and abroad, especially in aerospace engineering [4], target identification tracking [5][6][7], fault diagnosis [8][9][10][11][12], image fusion [13][14][15][16] and other fields. Therefore, researching target recognition and decision fusion has important theoretical significance and significant practical application value [17,18].…”

Section: Introductionmentioning

confidence: 99%

A New Multi-Sensor Target Recognition Framework based on Dempster-Shafer Evidence Theory

Wang¹

2018

IJPE

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In order to meet the higher requirements in military technology, automation, and intelligence, increasingly importance has been attached to the information fusion for multi-sensor systems. Dempster-Shafer evidence theory is a typical method of uncertainty information fusion due to its adjustability in uncertainty modeling; whereas classical evidence theory is still insufficient in solving high-conflict problems. This assumption studies the multi-sensor information fusion model based on evidence theory from the following aspects. First, it introduces the basic principles of evidence theory, and focuses on how to use triangular fuzzy numbers to obtain basic probability assignments. Second, the method of weighting the evidence using the reliability of the sensor is introduced. The reliability of the sensor is divided into two parts: static reliability and dynamic reliability. Moreover, this model proposes the irrationality of Deng's entropy weight for the binary target recognition problem, and improves the entropy weight in sensor dynamic weights. Finally, on the basis of the above research, sensor sensing data is applied to this model. Through simulation experiments, the validity of the model is proved and the target can be accurately identified.

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“…Chen et al [5] used EMD to decompose the bearing vibration signals and calculated the permutation entropy (PE) of the first few IMFs as the characteristic vector, and SVM was applied for operation status identification. Li et al [6] applied a multifractal method to extract the generalized dimensional spectral features from the vibration signals of hydropower units, and probabilistic neural network was used for fault diagnosis. However, these methods are largely dependent on prior knowledge about signal processing techniques and expert diagnosis experience.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering

Zhao

Jin

et al. 2020

Shock and Vibration

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Deep learning (DL) has been successfully used in fault diagnosis. Training deep neural networks, such as convolutional neural networks (CNNs), require plenty of labeled samples. However, in mechanical fault diagnosis, labeled data are costly and time-consuming to collect. A novel method based on a deep convolutional autoencoding network (DCAEN) and adaptive nonparametric weighted-feature extraction Gustafson–Kessel (ANW-GK) clustering algorithm was developed for the fault diagnosis of bearings. First, the DCAEN that is pretrained layer by layer by unlabeled samples and fine-tuned by a few labeled samples is applied to learn representative features from the vibration signals. Then, the learned representative features are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the low-dimensional main features are obtained. Finally, the low-dimensional features are input ANW-GK clustering for fault identification. Two datasets were used to validate the effectiveness of the proposed method. The experimental results show that the proposed method can effectively diagnose different fault types with only a few labeled samples.

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Fault Diagnosis for Machinery based on Feature Selection and Probabilistic Neural Network

Cited by 4 publications

References 10 publications

Ensembles for feature selection: A review and future trends

Ensembles for feature selection: A review and future trends

A New Multi-Sensor Target Recognition Framework based on Dempster-Shafer Evidence Theory

Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering

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