Detection of rotor fault in three-phase induction motor in case of low-frequency load oscillation

Göktaş, Taner; Arkan, Müslüm; Özgüven, Ömer Faruk

doi:10.1007/s00202-015-0342-5

Cited by 13 publications

(1 citation statement)

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“…For example, fluctuating loads can be present during motor operation and their resulting effects are very similar to those produced by the presence of a broken rotor bar [22]. Thus, when a motor drives a varying load, the identification of faults under this situation can be very difficult, and the common techniques can confuse the effect produced by the operating condition with a fault condition delivering an incorrect diagnostic for a healthy motor [23,24]. There exist classical methodologies that help to identify frequencies related to faults on induction motors, such as eccentricities, broken rotor bars, and bearing defects, among others, through signature analysis, but even they are limited by the load effects that obscure and overwhelm those produced by the fault conditions [25].…”

Section: Introductionmentioning

confidence: 99%

Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals

et al. 2023

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Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.

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