Applications of artificial intelligence techniques for induction machine stator fault diagnostics: review

Siddique, Aftab; Yadava, G.S.; Singh, Bhim

doi:10.1109/demped.2003.1234543

Cited by 90 publications

(46 citation statements)

References 45 publications

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“…ƒ b= (1  2s)ƒ (2.4) While the lower side band is specifically due to broken bar, the upper side band is due to consequent speed oscillation. In fact [13] shows that broken bars actually give rise to a sequence of such side bands given by ƒ b= ( 1  2k 1 s)ƒ , k 1 = 1,2,3… (2.5) and is depicted conceptually in fig( 2.5). Fig(2-5)Sideband freguncies around the fundamental line freguency.…”

Section: Broken Rotor Bar and End Ring Faultsmentioning

confidence: 99%

“…Torque and speed signals also contain 2sf and 4sf frequency components with broken rotor bars [13], [14]. detection of these faults are also possible by frequency domain analysis of shaft flux or more generally axial leakage flux which is monitored by using an external search coil wound around the shaft of a machine.…”

Section: Broken Rotor Bar and End Ring Faultsmentioning

confidence: 99%

“…Yazidi et al [12]observed that the techniques relying on the stator winding current analysis are not always reliable, especially when the number of shorted turns is small compared to the to the total number of turns in a phase winding. A. Siddique et al [13] They their focus his review of various AI techiques to the iduction motors, more specifically to the stator winding fault detection. X. Huang et al [14] propose a scheme to monitor voltage and current space vectors simultaneously in order to monitor the level of air-gap eccentricity in an induction motor .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Various Types of Faults and Their Detection Techniquesin Three Phase Induction Motors Fault

Alwan¹,

Sabbagh²

2017

IJERA

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Artificial neural networks are extensively used for speed, torque estimation, and solid state drive control in both DC and AC machines. These Artificial intelligent techniques are increasingly used for condition monitoring and fault detection of machines. this paper present an overview of researches onThree phase Induction Motors Faults Detection Using Artificial Neural Network(ANN) , a general classification and brief description of the focus area for research and development in this direction are given under title of various types of faults and their detections techniques an improvement in three-phase squirrel-cage induction machine bearing, stator, eccentricity ,inter-turn, end-ring, broken-bar faults detection and diagnosis based on a neural network approach is presented .Future research directions are also highlighted.

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Section: Broken Rotor Bar and End Ring Faultsmentioning

confidence: 99%

Section: Broken Rotor Bar and End Ring Faultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Various Types of Faults and Their Detection Techniquesin Three Phase Induction Motors Fault

Alwan¹,

Sabbagh²

2017

IJERA

View full text Add to dashboard Cite

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“…Popular AI techniques for machine diagnosis are ANNs, fuzzy logic systems, fuzzy-neural networks (FNNs), and neural-fuzzy systems. A review of recent developments in applications of AI techniques for induction machine stator fault diagnostics was given in [56].…”

Section: Pattern Recognition-based Approachesmentioning

confidence: 99%

Machine Fault Diagnosis and Prognosis: The State of The Art

Tran¹,

Yang²

2009

International Journal of Fluid Machinery and Systems

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Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

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Section: Introductionmentioning

confidence: 99%

Detection of Static Air-Gap Eccentricity in Three Phase induction Motor by Using Artificial Neural Network (ANN)

Alwan¹,

Farhan²,

Sabbagh³

2017

IJERA

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This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN) approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 Kw) have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA) based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between "healthy" and "faulty" machine.

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Applications of artificial intelligence techniques for induction machine stator fault diagnostics: review

Cited by 90 publications

References 45 publications

Various Types of Faults and Their Detection Techniquesin Three Phase Induction Motors Fault

Various Types of Faults and Their Detection Techniquesin Three Phase Induction Motors Fault

Machine Fault Diagnosis and Prognosis: The State of The Art

Detection of Static Air-Gap Eccentricity in Three Phase induction Motor by Using Artificial Neural Network (ANN)

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