Motor Current Signature Analysis for the Air Gap Eccentricity Detection In the Squirrel Cage Induction Machines

Yassa, Nacera; Rachek, M.; Houassine, Hamza

doi:10.1016/j.egypro.2019.04.027

Cited by 29 publications

(17 citation statements)

References 8 publications

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“…The simulation study was carried out using the validated mathematical model in [1]. Equations (6)(7)(8)(9)(10)(11)(12)(13)(14), as shown at the bottom of the next page, represent the final mathematical model in qd0 frame. The validated model has been implemented and simulated using MATLAB software.…”

Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

“…The use of MCS in fault detection basically depends on the decomposition of current into its harmonic components that are discriminating each type of fault. Since the current signature is sensitive to almost all motor faults, it is the most non-invasive technique used in machine faults diagnosis [14]- [16]. Moreover, the use of MCS is preferred over the other signatures since it does not need additional measurement sensors [17].…”

Section: Introductionmentioning

confidence: 99%

“…represent the stator and rotor voltages equations. Equations(8)(9)(10)(11)(12)(13) represent the flux-current relations in matrix form while(14) represents the torque equation. where µ is the shorted turns ratio.v s q , vs d and v s 0 are qd0 stator voltages.…”

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confidence: 99%

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Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

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Stator inter-turn fault diagnosis system for electric motors is of a considerable concern due to its significant effect on industrial production. In this paper, a new method for detecting the inter-turn fault and quantifying its severity in the line start permanent magnet synchronous motor (LSMPSM) is proposed. The new method depends on monitoring the stator current during steady-state period to detect the fault. The convolutional neural network (CNN) method is proposed to correlate the motor steady-state current with the status of the motor winding conditions and detect any presence of inter-turn faults. The data used in this study is extracted from both an experimental setup of a one-horsepower LSPMSM and the corresponding verified mathematical model through several testing cases under various loading conditions. One of the main features of the proposed technique is that it does not require separate feature extraction phase. The results indicate that the proposed technique is able to detect the inter-turn fault under different loading conditions varies from 0NM to 4NM with accuracy of 97.75% for all defined fault levels. The use of steady-state current for fault detection regardless of motor load enables the proposed technique to detect the fault online without disturbing the system functionality and reliability as well as without adding any extra hardware to the system. INDEX TERMS Convolutional neural network (CNN), diagnosis, fault detection, inter-turn fault, LSPMSM.

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Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

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“…Using the circuit transformation tensor C i in (34), which transforms branch resistances into loop ones, as in [57,58], the resistance matrix of the rotor R r that includes the current constraints (32) and 33is…”

Section: Current Constraints In a Squirrel Cage Immentioning

confidence: 99%

“…In particular, the eccentricity fault [32,33] gives rise to a non-uniform air gap length, which becomes a function of the angular coordinate. Moreover, this function can be different for each rotor position [34]. To overcome this difficulty, the analytical methods for calculating the inductance matrix commonly apply the simplification of considering a sinusoidal distribution of the spatial waves in the air-gap, thus limiting the calculation of the inductances to its fundamental harmonic component.…”

Section: Introductionmentioning

confidence: 99%

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Martínez-Román

Puche-Panadero

Sapena-Bañó

et al. 2020

Sensors

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Induction machines (IMs) are critical components of many industrial processes, what justifies the use of condition-based maintenance (CBM) systems for detecting their faults at an early stage, in order to avoid costly breakdowns of production lines. The development of CBM systems for IMs relies on the use of fast models that can accurately simulate the machine in faulty conditions. In particular, IM models must be able to reproduce the characteristic harmonics that the IM faults impress in the spatial waves of the air gap magneto-motive force (MMF), due to the complex interactions between spatial and time harmonics. A common type of fault is the eccentricity of the rotor core, which provokes an unbalanced magnetic pull, and can lead to destructive rotor-stator rub. Models developed using the finite element method (FEM) can achieve the required accuracy, but their high computational costs hinder their use in online CBM systems. Analytical models are much faster, but they need an inductance matrix that takes into account the asymmetries generated by the eccentricity fault. Building the inductance matrix for eccentric IMs using traditional techniques, such as the winding function approach (WFA), is a highly complex task, because these functions depend on the combined effect of the winding layout and of the air gap asymmetry. In this paper, a novel method for the fast and simple computation of the inductance matrix for eccentric IMs is presented, which decouples the influence of the air gap asymmetry and of the winding configuration using two independent tensors. It is based on the construction of a primitive inductance tensor, which formulates the eccentricity fault using single conductors as the simplest reference frame; and a winding tensor that converts it into the inductance matrix of a particular machine, taking into account the configuration of the windings. The proposed approach applies routine procedures from tensor algebra for performing such transformation in a simple way. It is theoretically explained and experimentally validated with a commercial induction motor with a mixed eccentricity fault.

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