Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques

2011 ◽  
Vol 35 (5) ◽  
pp. 689-704 ◽  
Author(s):  
Isaac Monroy ◽  
Kris Villez ◽  
Moisès Graells ◽  
Venkat Venkatasubramanian
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Comparative Study ◽  
Fermentation Process ◽  
Classification Techniques

scholarly journals SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES

2020 ◽  
Vol 5 (1) ◽  
pp. 28-45
Author(s):  
Nehal Hamdy Al-banhawy ◽  
◽  
Heba Mohsen ◽  
Neveen Ghali ◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Comparative Study ◽  
Active Area ◽  
Signature Verification ◽  
Classification Techniques ◽  
Verification Systems ◽  
Identification Techniques ◽  
Handwritten Signature ◽  
Verification Techniques

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

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