scholarly journals Induction Motor Multiclass Fault Diagnosis Based on Mean Impact Value and PSO-BPNN

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Chun-Yao Lee ◽  
Hong-Yi Ou
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Selection Process ◽  
Signal To Noise Ratio ◽  
Computing Time ◽  
Back Propagation ◽  
Selection Model ◽  
Current Signal ◽  
Model Based

This paper presents a feature selection model based on mean impact value (MIV) to solve induction motor (IM) fault diagnosis on the current signal. In this paper, particle swarm optimization (PSO) is combined with back propagation neural network (BPNN) to classify the current signal of IM. First, the purpose of this study is to establish IM fault diagnosis system. Additionally, this study proposes a feature selection process that is composed of MIV, whose objective is to reduce the number of classifier input features. Secondly, the features are extracted as a feature database after analyzing the current signal of IM, and the fault diagnosis is established through the model of PSO-BPNN. Finally, redundant features are deleted through this feature selection process and a classifier is built. The result shows that the feature selection model based on MIV can filter the features effectively at a signal to noise ratio of 30 dB and 20 dB for the IM fault detection problem. In addition, the computing time of BPNN is also reduced which is helpful for online detection.

Decent fault classification of VFD fed induction motor using random forest algorithm

2020 ◽  
Vol 34 (4) ◽  
pp. 492-504
Author(s):  
Parth Sarathi Panigrahy ◽  
Deepjyoti Santra ◽  
Paramita Chattopadhyay
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Induction Motor ◽  
Selection Process ◽  
Selection Criterion ◽  
Feature Model ◽  
Fault Classification ◽  
Feature Selection Technique ◽  
Steady State Condition

AbstractA data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.

High-content Analysis in Monastrol Suppressor Screens

2011 ◽  
Vol 50 (03) ◽  
pp. 265-272 ◽  
Author(s):  
Y. Ge ◽  
D. Zhang ◽  
X. Zhou ◽  
Z. Zhang
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Image Segmentation ◽  
Selection Process ◽  
Fluorescent Microscopy ◽  
Back Propagation ◽  
Principal Component ◽  
Current Approach ◽  
Bipolar Cells ◽  
Cell Phenotype

SummaryObjectives: High-content screening (HCS) via automated fluorescent microscopy is a powerful technology for the effective expression of cellular processes. However, HCS will generally produce tremendous image datasets, which leads to difficulties of handling and analyzing. We proposed an automatic classification approach for simultaneous feature extraction and cell phenotype recognition of monoaster and bipolar cells in HCS system.Methods: The proposed approach was composed of image segmentation, feature extraction, and classification. The image segmentation was based on the Laplacian of Gaussian (LoG) edge detection method. For the reduction of noise effect on cellular images, we employed an adaptive threshold in microtubule channel. The principal component analysis was used in the feature selection process. The classification was performed with a back-propagation neural network (BPNN). Using the current approach, the cell phases were distinguished from three-channel acquisitions of cellular images and the numbers of bipolar and monoaster cells were automatically counted.Results: The validity of this approach was examined by the application of screening the response of drug compounds in suppressing Monastrol. Our results indicate that the proposed algorithm could improve the recognition rates of monoaster and bipolar cells to 97.98% and 93.12%, respectively, compared with 97.02% and 86.96% obtained from the same samples by multi-phenotypic mitotic analysis (MMA).Conclusions: We have shown that BPNN is a valuable tool to classify cell phenotype. To further improve the classification performance, more test data, more optimized feature selection approaches, and advanced classifier may be required and will be investigated in future works.

scholarly journals An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1666
Author(s):  
Muataz Salam Al-Daweri ◽  
Khairul Akram Zainol Ariffin ◽  
Salwani Abdullah ◽  
Mohamad Firham Efendy Md. Senan
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Rough Set Theory ◽  
Technology Development ◽  
Detection System ◽  
Selection Process ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Selection Of

The significant increase in technology development over the internet makes network security a crucial issue. An intrusion detection system (IDS) shall be introduced to protect the networks from various attacks. Even with the increased amount of works in the IDS research, there is a lack of studies that analyze the available IDS datasets. Therefore, this study presents a comprehensive analysis of the relevance of the features in the KDD99 and UNSW-NB15 datasets. Three methods were employed: a rough-set theory (RST), a back-propagation neural network (BPNN), and a discrete variant of the cuttlefish algorithm (D-CFA). First, the dependency ratio between the features and the classes was calculated, using the RST. Second, each feature in the datasets became an input for the BPNN, to measure their ability for a classification task concerning each class. Third, a feature-selection process was carried out over multiple runs, to indicate the frequency of the selection of each feature. From the result, it indicated that some features in the KDD99 dataset could be used to achieve a classification accuracy above 84%. Moreover, a few features in both datasets were found to give a high contribution to increasing the classification’s performance. These features were present in a combination of features that resulted in high accuracy; the features were also frequently selected during the feature selection process. The findings of this study are anticipated to help the cybersecurity academics in creating a lightweight and accurate IDS model with a smaller number of features for the developing technologies.

A Novel Feature Selection Method for Process Fault Diagnosis

2013 ◽  
Vol 427-429 ◽  
pp. 2045-2049
Author(s):  
Chun Mei Yu ◽  
Sheng Bo Yang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Selection Process ◽  
Feature Selection Method ◽  
Principal Component ◽  
Selection Method ◽  
Fault Classification ◽  
Process Data ◽  
Bhattacharyya Distance

To increase fault classification performance and reduce computational complexity,the feature selection process has been used for fault diagnosis.In this paper, we proposed a sparse representation based feature selection method and gave detailed procedure of the algorithm. Traditional selecting methods based on wavelet package decomposition and Bhattacharyya distance methods,and sparse methods, including sparse representation classifier, sparsity preserving projection and sparse principal component analysis,were compared to the proposed method.Simulations showed the proposed selecting method gave better performance on fault diagnosis with Tennessee Eastman Process data.

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