The nature of statistical learning theory
The nature of statistical learning theory
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Information Sciences: an International Journal
Artificial neural network approach for fault detection in rotary system
Applied Soft Computing
Car assembly line fault diagnosis based on robust wavelet SVC and PSO
Expert Systems with Applications: An International Journal
Fault diagnosis of ball bearings using machine learning methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using thermal image matter-element to design a circuit board fault diagnosis system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of multiclass support vector machines for fault diagnosis of field air defense gun
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
International Journal of Data Analysis Techniques and Strategies
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
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Fault detection and diagnosis have an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance technique that is applicable in the fault diagnosis of rotating machinery faults. A unique flexible algorithm is proposed for classifying the condition of centrifugal pump based on support vector machine hyper-parameters optimization and artificial neural networks (ANNs) which are composed of eight distinct steps. Artificial neural networks (ANNs), support vector classification with genetic algorithm (SVC-GA) and support vector classification with particle swarm optimization (SVC-PSO) algorithm have been considered in a flexible algorithm to perform accurate classification in the manufacturing area. SVC-GA, SVC-PSO and ANN have been used together due to their importance and capabilities in classifying domain. Also, the superiority of the proposed hybrid algorithm (SVC with GA and PSO) is shown by comparing its results with SVC performance. Two types of faults through six features, flow, temperature, suction pressure, discharge pressure, velocity, and vibration, have been classified with proposed integrated algorithm. To test the robustness of the efficiency results of the proposed method, the ability of proposed flexible algorithm in dealing with noisy and corrupted data is analyzed.