A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probabi...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Energies Ročník 12; číslo 11; s. 2105
Hlavní autori: Esakimuthu Pandarakone, Shrinathan, Mizuno, Yukio, Nakamura, Hisahide
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.06.2019
Predmet:
Algorithms
Artificial intelligence
Discriminant analysis
Failure analysis
Fault diagnosis
Forecasting techniques
Fourier transforms
frequency spectrum analysis
induction motor
induction motor; minor bearing fault; frequency spectrum analysis; machine learning algorithm; artificial intelligence
Machine learning
machine learning algorithm
Methods
minor bearing fault
Neural networks
T
Technology
Vibration analysis
United States > US
Japan
ISSN:1996-1073, 1996-1073
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en12112105