Early Detection and Diagnosis of Faults in an AC Motor Using Neuro Fuzzy Techniques: FasArt + Fuzzy k Nearest Neighbors

Santamarı́a, Javier; Sainz-Palmero, G.I.; Torre, Eduardo Julio Moya de la; González, José Ramón Perán

doi:10.1007/3-540-45723-2_69

Cited by 6 publications

(1 citation statement)

References 7 publications

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“…In [89] and [90], multiple ANFIS algorithm and its combination with GAs were employed for detecting rotor bar breakage, air gap eccentricity faults of three-phase induction motor and rolling bearings, respectively. Other applications of ANFIS for machine fault diagnosis were introduced in [91][92][93][94].…”

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: 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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An approach for bearing fault detection in electrical motors

Gülmezoğlu

Ergin

2007

Int Trans Elec Energy Syst

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SUMMARYIn this paper, a novel approach for fault detection and diagnosis of induction motors is proposed. This approach depends on determination of a common vector for each class, called the common vector approach (CVA). The common vector of each class represents invariant features or common properties of that class. Vibration signals measured from the rotor ball bearings of an induction motor are used in the experimental study. Experimental results indicate that the proposed approach can be efficiently used for the analysis of faulty conditions in induction motors.

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