ANN based fault diagnosis of permanent magnet synchronous motor under stator winding shorted turn

Moosavi, Seyed Saeid; Djerdir, Abdesslem; Amirat, Youcef; Khaburi, Davood Arab

doi:10.1016/j.epsr.2015.03.024

Cited by 87 publications

(43 citation statements)

References 19 publications

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“…Recently in Ref. [24], researchers focused on the development of a solution to identify stator inter turn shortcircuit in a permanent magnet synchronous motor. A Multilayer Perceptron network was used for the diagnosis and classification of different levels of short-circuit and check its severity under variable speed and load torque conditions.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Palácios

Silva

Goedtel

et al. 2015

Electric Power Systems Research

111

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Section: Introductionmentioning

confidence: 99%

A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Palácios

Silva

Goedtel

et al. 2015

Electric Power Systems Research

111

View full text Add to dashboard Cite

“…Therefore, the optimal affine transformation matrix has the form = Θ , and = ( − Θ Θ ), where Θ is the Moor-Penrose generalized inverse of Θ . Let = , , … , and be the 0-1 selection matrix, such that = , then the objective function is converted to this form: According to the numerical analysis in [17], Wi can also be written as follows:…”

Section: The Algorithm Of Lltsamentioning

confidence: 99%

“…Second, the main eigenvectors with low dimension and easy of identification are extracted from the high-dimensional fault feature set by applying an appropriate dimensionality reduction method, such as Principle component analysis (PCA) [11], Locality Pre-serving Projection (LPP) [12], Linear Discriminant Analysis (LDA) [13]. Thirdly, the low-dimensional feature set is inputted into a learning machine for pattern recognition, for example, K nearest neighbor classifier (KNNC) [14,15], artificial neural network (ANN) [16,17] and sup-port vector machine (SVM) [18,19]. Application of this fault recognition method for looseness extent of fan foundation will face the following problems: 1) loose-ness feature extraction, and non-sensitive or poor sensitive feature interference.…”

Section: Introductionmentioning

confidence: 99%

Pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning

Chen¹,

Mu²,

Yang³

et al. 2016

J. vibroeng.

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To realize automation and high accuracy of pedestal looseness extent recognition for rotating machinery, a novel pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning dimension reduction is proposed. Firstly, the pedestal looseness extent of rotating machinery is characterized by vibration signal of rotating machinery and its spectrum, then the time-frequency features are extracted from vibration signal to construct the origin looseness extent feature set. Secondly, the algorithm of looseness sensitivity index is designed to filter out the non-sensitive feature and poor sensitivity feature from the origin looseness extent feature set, avoiding the interference of non-sensitive and poor sensitivity feature. The sensitive features are selected to construct the looseness extent sensitive feature set, which has stronger characterization capabilities than the origin looseness extent feature set. Moreover, an effective manifold learning method called linear local tangent space alignment (LLTSA) is introduced to compress the looseness extent sensitive feature set into the low-dimensional looseness extent sensitive feature set. Finally, the low-dimensional looseness extent sensitive feature set is inputted into weight K nearest neighbor classifier (WKNNC) to recognize the different pedestal looseness extents of rotating machinery, the WKNNC's recognition accuracy is more stable compared with that of a k nearest neighbor classification (KNNC). At the same time, the pedestal looseness extent recognition of rotating machinery is realized. The feasibility and validity of the present method are verified by successful pedestal looseness extent recognition application in a rotating machinery.

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“…However, this work focuses on conditions covering healthy and faulty situations, including fault detection and identification. Moreover, it is worth noting that artificial intelligence-based methodologies have been increasingly applied to rotating machinery fault detection and condition monitoring nowadays [35][36][37]. These techniques can be used in future works as auxiliary tools to complement the fault diagnosis by using the proposed methodology, being not in the scope of the present work.…”

Section: Introductionmentioning

confidence: 99%

A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems

Salomon

Ferreira

Sant’Ana³

et al. 2019

Energies

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The condition of synchronous generators (SGs) is a matter of great attention, because they can be seen as equipment and also as fundamental elements of power systems. Thus, there is a growing interest in new technologies to improve SG protection and maintenance schemes. In this context, electrical signature analysis (ESA) is a non-invasive technique that has been increasingly applied to the predictive maintenance of rotating electrical machines. However, in general, the works applying ESA to SGs are focused on isolated machines. Thus, this paper presents a study on the condition monitoring of SGs in bulk electric systems by using ESA. The main contribution of this work is the practical results of ESA for fault detection in in-service SGs interconnected to a power system. Two types of faults were detected in an SG at a Brazilian hydroelectric power plant by using ESA, including stator electrical unbalance and mechanical misalignment. This paper also addresses peculiarities in the ESA of wound rotor SGs, including recommendations for signal analysis, how to discriminate rotor faults on fault patterns, and the particularities of two-pole SGs.

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ANN based fault diagnosis of permanent magnet synchronous motor under stator winding shorted turn

Cited by 87 publications

References 19 publications

A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning

A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems

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