Detection of Eccentricity Faults in Induction Machines Based on Nameplate Parameters
ABSTRACT Eccentricity-related faults in induction motors have been studied extensively over the last few decades. They can exist in the form of static or dynamic eccentricity or both, in which case it is called a mixed eccentricity fault. These faults cause bearing damage, excessive vibration and noise, unbalanced magnetic pull, and under extreme conditions, stator-rotor rub which may seriously damage the motors. Since eccentricity faults are often associated with large induction machines, the repair or replacement costs arising out of such a scenario may easily run into tens and thousands of dollars. Previous research works have shown that it is extremely difficult to detect such faults if they appear individually, rather than in mixed form, unless the number of rotor bars and the pole-pair number conform to certain relationships. In this paper, it is shown that the terminal voltages of induction machines at switch-off reveal certain features that can lead to the detection of these faults in individual form, even in machines that do not show these signatures in line-current spectrum in steady state, or to the detection of the main contributory factor in case of mixed eccentricity.
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ABSTRACT: The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years. Reducing maintenance costs and preventing unscheduled downtimes, which result in losses of production and financial incomes, are the priorities of electrical drives manufacturers and operators. In fact, both correct diagnosis and early detection of incipient faults lead to fast unscheduled maintenance and short downtime for the process under consideration. They also prevent the harmful and sometimes devastating consequences of faults and failures. This topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years. The total number of operating electrical machines in the world was around 16.1 billion in 2011, with a growth rate of about 50% in the last five years .IEEE Industrial Electronics Magazine 06/2014; 8(2):31-42. · 5.06 Impact Factor
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ABSTRACT: Mostly the faults in electrical machines are related with the bearings. Thus, a reliable bearing condition monitoring scheme able to detect either local or distributed defects are mandatory to avoid a breakdown in the machine. So far, the research has been carried out mainly in the detection of local faults, such as balls and raceways faults, but surface roughness is not so reported. This paper deals with a novel and reliable scheme capable to detect any fault that may occur in a bearing, based on EXIN Curvilinear Component Analysis, CCA, and Neural Network. The EXIN CCA, which is an improvement of the Curvilinear Component Analysis, has been conceived for data visualization, interpretation and classification for real time industrial applications. The effectiveness of this condition monitoring scheme has been verified by experimental results obtained from different operation conditions.Electrical Machines (ICEM), 2012 XXth International Conference on; 01/2012
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ABSTRACT: An intelligent approach artificial neural network (ANN) combined with genetic approach (GA) is presented for detection of stator winding related fault of switched reluctance machine. Switched reluctance machine (SRM) is known to be fault tolerant, however, is not fault free, and questions emerge as to powerful diagnostic methods. This paper takes an in-depth look at winding open-circuits ‘the worst case’ in this particular machine. Various cases are considered, falling in two distinct categories: one when an entire phase is opened and the other when only part of a winding is opened. Therefore, application of classification method is very necessary to get the exact information to classify and to obtain a more complete labelling, and so, a more powerful diagnosis. An appropriate features extraction and features selection techniques should be incorporated. In this proposed method, smoothing Time-Frequency Representation (TFR) from a time-frequency ambiguity plane is used to extract features from torque time signals. In order to reduce the number of the features, a GA is suggested to select optimal ones. The new features provide more sensitive information for a classifier. The proposed features feed a simple non-linear classifier based ANN which separates almost perfectly between normal and faulty conditions, with also very high diagnostic accuracy between the faulty classes. Experiments are carried out using a laboratory apparatus to show the how various configurations of the system are able to detect different classes of faults. The proposed method successfully distinguished the difference, and classified SRM open-circuit faults correctly.40th Annual Conference of IEEE Industrial Electronics Society, Dallas, TX - USA; 11/2014