A comparative analysis of artificial intelligence-based methods for fault diagnosis of mechanical systems

• Autorzy: Moghaddam Reihaneh Kardehi , Yazdan Navid Moshtaghi

Identyfikatory

DOI

10.2478/mme-2019-0016

• Języki publikacji: EN

Abstrakty

EN

The present research studied fault diagnosis of composite sheets using vibration signal processing and artificial intelligence (AI)-based methods. To this end, vibration signals were collected from sound and faulty composite plates. Using different time-frequency signal analysis and processing methods, a number of features were extracted from these signals and the most effective features containing further information on these composite plateswere provided as input to different classification systems. The output of these classification systems reveals the faults in composite plates. The different types of classification systems used in this research were the support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (k-NN), artificial neural networks (ANNs), Extended Classifier System (XCS) algorithm, and the proposed improved XCS algorithm. The research results were reflective of the superiority of ANFIS in terms of precision,while this method had the highest process duration with an equal number of iterations. The precision of the proposed improved XCS methodwas lower than that of ANFIS, but the duration of the processwas shorter than the ANFIS method with an equal number of iterations.

Słowa kluczowe

EN

composite plate; XCS; signal processing; artificial intelligence

PL

płyta kompozytowa; XCS; przetwarzanie sygnałów; sztuczna inteligencja

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Mechanics and Mechanical Engineering
• Rocznik: 2019
• Tom: Vol. 23, nr 1
• Strony: 113--124
• Opis fizyczny: Bibliogr. 28 poz., il. kolor., fot., wykr.

Twórcy

autor

Moghaddam Reihaneh Kardehi

• Department of Electrical Engineering, Mashhad branch, Islamic Azad University, Mashhad, Iran

autor

Yazdan Navid Moshtaghi

• Department of Electrical Engineering, Mashhad branch, Islamic Azad University, Mashhad, Iran

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).