Application of AI tools in fault diagnosis of electrical machines and drives-an overview

Awadallah, Mohammed A.; Morcos, M.M.

doi:10.1109/tec.2003.811739

Cited by 187 publications

(75 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We will consider mechanical and electrical machines (Awadallah & Morcos 2003, Benbouzid et al,1999, and as a consequence our intend is to develop FDI methods for wind turbines and renewable multi-source energy systems (Guérin et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, data-based methods require a lot of measurements and can also be divided into signal processing methods and artificial intelligence approaches. Many researchers have performed fault detection by using vibration analysis for mechanical systems, or current and voltage signature analysis for electromechanical systems (Awadallah & Morcos 2003), (Benbouzid et al, 1999). Other researchers use the artificial intelligence (AI) tools for faults diagnosis (Awadallah & Morcos 2003) and the frequency methods for faults detection and isolation (Benbouzid et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have performed fault detection by using vibration analysis for mechanical systems, or current and voltage signature analysis for electromechanical systems (Awadallah & Morcos 2003), (Benbouzid et al, 1999). Other researchers use the artificial intelligence (AI) tools for faults diagnosis (Awadallah & Morcos 2003) and the frequency methods for faults detection and isolation (Benbouzid et al, 1999). This study continues our research in frequency domain, concerning fault detection by means of filters bank , (Mustapha et al-b, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault Detection Algorithm Based on Filters Bank Derived from Wavelet Packets

Mustapha¹,

Khalil²,

Hoblos³

et al. 2008

Robotics Automation and Control

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Detection Algorithm Based on Filters Bank Derived from Wavelet Packets

Mustapha¹,

Khalil²,

Hoblos³

et al. 2008

Robotics Automation and Control

View full text Add to dashboard Cite

“…The superior features of the MCSA method are that it is unaffected by loading and other imbalanced situations and that the harmonic components that result from electrical and mechanical faults become more visible [12,13]. An inter-turn short circuit fault is the most important fault type [14]. There are several fault detection methods for inter-turn short circuit faults, using current, voltage, axial flux, and d-q component analyses [15,16].…”

Section: Introductionmentioning

confidence: 99%

Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a Pattern Recognition System

Çira¹,

Arkan²,

Gümüş³

2016

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

show abstract

“…The development of AI technologies gives one an opportunity to use them not only for conventional applications (expert systems, intelligent databases [1], technical diagnostics [2,3] etc.) but also for automation of mechanical manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Micromechanics as a Testbed for Artificial Intelligence Methods Evaluation

Kussul

Baidyk

Lara-Rosano

et al.

Professional Practice in Artificial Intelligence

View full text Add to dashboard Cite

Abstract. Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller overall sizes and therefore of lower cost. This equipment can be used for evaluation of different AI methods in an easy and inexpensive way. The methods that show good results can be transferred to the industry through appropriate scaling. This paper contains brief description of low cost microequipment prototypes and some AI methods that can be evaluated with mentioned prototypes.

show abstract

Application of AI tools in fault diagnosis of electrical machines and drives-an overview

Cited by 187 publications

References 62 publications

Fault Detection Algorithm Based on Filters Bank Derived from Wavelet Packets

Fault Detection Algorithm Based on Filters Bank Derived from Wavelet Packets

Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a Pattern Recognition System

Micromechanics as a Testbed for Artificial Intelligence Methods Evaluation

Contact Info

Product

Resources

About