Fault Classification of a Centrifugal Pump in Normal and Noisy Environment with Artificial Neural Network and Support Vector Machine Enhanced by a Genetic Algorithm

2015 ◽  
pp. 58-70 ◽  
Author(s):  
Abtin Nourmohammadzadeh ◽  
Sven Hartmann
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Centrifugal Pump ◽  
Fault Classification ◽  
Support Vector ◽  
Noisy Environment ◽  
Artificial Neural

scholarly journals Fault Diagnosis in Centrifugal Pump using Support Vector Machine and Artificial Neural Network

2021 ◽  
Author(s):  
Nagendra Singh Ranawat ◽  
◽  
Pavan Kumar Kankar ◽  
Ankur Miglani ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Centrifugal Pump ◽  
Support Vector ◽  
Feature Ranking ◽  
Chi Square ◽  
Artificial Neural

Centrifugal pumps are commonly utilized in thermo-fluidic systems in the industry. Being a rotating machinery, they are prone to vibrations and their premature failure may affect the system predictability and reliability. To avoid their premature breakdown during operation, it is necessary to diagnose the faults in a pump at their initial stage. This study presents the methodology to diagnose fault of a cent rifugal pump using two distinct machine learning techniques, namely, Support vector machine (SVM) and Artificial neural network (ANN). Different statistical features are extracted in the time and the frequency domain of the vibration signal for different working conditions of the pump. Furthermore, to decrease the dimensionality of the obtained features different feature ranking (FR) methods, namely, Chi-square, ReliefF and XGBoost are employed. ANN technique is found to be more efficient in classifying faults in a centrifugal pump as compared to the SVM, and Chi-square and XGBoost ranking techniques are better than ReliefF at sorting more relevant features. The results presented in thus study demonstrate that an ANN based machine learning approach with Chi-square and XGBoost feature ranking techniques can be used effectively for the fault diagnosis of a centrifugal pump.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

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