Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

Sapena-Bañó, Ángel; Martínez-Román, Javier; Puche-Panadero, Rubén; Pineda-Sánchez, Manuel; Pérez-Cruz, J.; Riera-Guasp, M.

doi:10.1016/j.ijepes.2018.03.001

Cited by 30 publications

(46 citation statements)

References 147 publications

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“…Therefore, Equation ( 48 ) can be reduced to a simple element-wise multiplication of three vectors in the frequency domain, using the FFT (and its inverse, the IFFT), as

where the superscript

stands for the conjugate of each vector element, and the operator ∘ stands for the element-wise or Hadamard product of two vectors (written as “*” in MatLab environment). This equation is similar to the one presented in [ 72 ], where the convolution theorem has been applied to obtain the mutual inductances between two phases, based on the magnetomotive force generated by a single conductor, instead of the MVP.…”

Section: Computation Of the Mutual Inductances Between Two Phases mentioning

confidence: 99%

Partial Inductance Model of Induction Machines for Fault Diagnosis

Pineda-Sánchez

Puche-Panadero

Martínez-Román

et al. 2018

Sensors

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The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models used for healthy machines, because one of the effect of the faults is to change the winding configurations (broken bar faults, rotor asymmetries, and inter-turn short circuits) or the magnetic circuit (eccentricity and bearing faults). This produces a change of the self and mutual phase inductances, which induces in the stator currents the characteristic fault harmonics used to detect and to quantify the fault. The development of a machine model that can reflect these changes is a challenging task, which is addressed in this work with a novel approach, based on the concept of partial inductances. Instead of developing the machine model based on the phases’ coils, it is developed using the partial inductance of a single conductor, obtained through the magnetic vector potential, and combining the partial inductances of all the conductors with a fast Fourier transform for obtaining the phases’ inductances. The proposed method is validated using a commercial induction motor with forced broken bars.

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“…Therefore, Equation ( 48 ) can be reduced to a simple element-wise multiplication of three vectors in the frequency domain, using the FFT (and its inverse, the IFFT), as

where the superscript

Section: Computation Of the Mutual Inductances Between Two Phases mentioning

confidence: 99%

Partial Inductance Model of Induction Machines for Fault Diagnosis

Pineda-Sánchez

Puche-Panadero

Martínez-Román

et al. 2018

Sensors

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“…In case of IMs with uniform air gap, as represented in Figure 2, L mc ij depends only on the angular separation between conductors i and j, as given in [49]…”

Section: The Primitive Inductance Tensor Of a Non-eccentric Immentioning

confidence: 99%

“…Figure 7 shows the three first columns of the winding tensor C c (14), corresponding to the stator phases, and Figure 8 shows the next three columns of C c (14), corresponding to the three first rotor loops. The inclination of rotors slots has been taken into account as in [49]. (14), corresponding to the three first rotor loops of the IM given in Appendix A, which contains the current-sheet generated by each rotor loop when fed by a unit current.…”

Section: Winding Tensor Of the Healthy Immentioning

confidence: 99%

“…In case of IMs with uniform air gap, as represented in Figure 2 ,

depends only on the angular separation between conductors i and j , as given in [ 49 ]

where

T·m·A −1 , l is the effective length of the stator bore, r is the radius at the center of the air gap, and g is the air gap length.…”

Section: Computation Of the Mutual Inductance Matrix Using Tensor mentioning

confidence: 99%

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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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“…Therefore, the reduction and prediction of faults occurring in electrical machines and drive systems such as electrical, thermal, and mechanical faults of electrical machines are strongly suggested to be essential [23][24][25][26][27][28][29]. Classical solutions of Fault Detection and Diagnosis (FDD) [30] are based on the complex mathematical models, e.g., supervised diagnosis [25][26][27][28][29]31], or dynamic models [32][33][34][35][36][37] of the processing system. Intelligent modernization has contributed to the widespread use of Machine Learning (ML) techniques in industrial applications [38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

Cited by 30 publications

References 147 publications

Partial Inductance Model of Induction Machines for Fault Diagnosis

Partial Inductance Model of Induction Machines for Fault Diagnosis

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

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

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