Induction machine model with space harmonics for the diagnosis of rotor eccentricity, 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.2019.105625

Cited by 16 publications

(19 citation statements)

References 63 publications

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“…The most time-efficient of the compared methods is the AS. On the other hand, as it can be observed comparing Figures 3,5,7,11,19, and 23, the wavelet scalogram produces a smoother representation of the IF of the fault harmonics.…”

Section: Definition Of a Fault Signature Based On The If Of The Faultmentioning

confidence: 86%

“…Some of the most common induction machine faults and their characteristic signatures are: Rotor asymmetries, which generate fault related harmonics at frequencies given by [ 16 , 17 ]:

where s is the slip,

being the synchronous speed of the machine and n its actual speed), and

is the power supply frequency. Mixed eccentricity, which generates fault harmonics at frequencies given by [ 18 , 19 , 20 ]

where

is the rotational frequency of the motor, and p is the pole pairs number. Cyclic faults in the outer or inner races of bearings, which generate fault harmonics at frequencies that depend on the fault localization (inner or outer race), and on the bearing characteristics (number of balls, ball diameter, bearing pitch diameter, and the contact angle) [ 21 , 22 ], given by

for the outer and inner race, respectively, where

is the number of bearing balls.…”

Section: Introductionmentioning

confidence: 99%

“…Mixed eccentricity, which generates fault harmonics at frequencies given by [ 18 , 19 , 20 ]

where

is the rotational frequency of the motor, and p is the pole pairs number.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Puche-Panadero

Martínez-Román

Sapena-Bañó

et al. 2020

Sensors

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Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms—as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform—which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.

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Section: Definition Of a Fault Signature Based On The If Of The Faultmentioning

confidence: 86%

“…Some of the most common induction machine faults and their characteristic signatures are: Rotor asymmetries, which generate fault related harmonics at frequencies given by [ 16 , 17 ]:

where s is the slip,

being the synchronous speed of the machine and n its actual speed), and

is the power supply frequency. Mixed eccentricity, which generates fault harmonics at frequencies given by [ 18 , 19 , 20 ]

where

for the outer and inner race, respectively, where

is the number of bearing balls.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Puche-Panadero

Martínez-Román

Sapena-Bañó

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The expression of the mutual inductance between conductors

has also been obtained considering an analytic two dimensional field analysis in [ 25 , 48 ], or a numerical model in [ 47 ]. Besides, it has been obtained in the case of a non-uniform air gap due to rotor eccentricity in [ 29 , 49 ].…”

Section: Primitive Reference Frame Of the Scimmentioning

confidence: 99%

Analytical Model of Induction Machines with Multiple Cage Faults Using the Winding Tensor Approach

Martínez-Román

Puche-Panadero

Sapena-Bañó

et al. 2021

Sensors

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Induction machines (IMs) are one of the main sources of mechanical power in many industrial processes, especially squirrel cage IMs (SCIMs), due to their robustness and reliability. Their sudden stoppage due to undetected faults may cause costly production breakdowns. One of the most frequent types of faults are cage faults (bar and end ring segment breakages), especially in motors that directly drive high-inertia loads (such as fans), in motors with frequent starts and stops, and in case of poorly manufactured cage windings. A continuous monitoring of IMs is needed to reduce this risk, integrated in plant-wide condition based maintenance (CBM) systems. Diverse diagnostic techniques have been proposed in the technical literature, either data-based, detecting fault-characteristic perturbations in the data collected from the IM, and model-based, observing the differences between the data collected from the actual IM and from its digital twin model. In both cases, fast and accurate IM models are needed to develop and optimize the fault diagnosis techniques. On the one hand, the finite elements approach can provide highly accurate models, but its computational cost and processing requirements are very high to be used in on-line fault diagnostic systems. On the other hand, analytical models can be much faster, but they can be very complex in case of highly asymmetrical machines, such as IMs with multiple cage faults. In this work, a new method is proposed for the analytical modelling of IMs with asymmetrical cage windings using a tensor based approach, which greatly reduces this complexity by applying routine tensor algebra to obtain the parameters of the faulty IM model from the healthy one. This winding tensor approach is explained theoretically and validated with the diagnosis of a commercial IM with multiple cage faults.

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“…The purpose is to maintain fast and accurate models for the induction motor state. These models are self-training, Expert system for monitoring and identifying these failures [30].…”

Section: Literature Reviewmentioning

confidence: 99%

The Investigation of Motor Production Test Indicators in Qualitative Research Methods

Lin¹,

Yeh²,

Yang³

et al. 2020

IJMMM

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This research is based on the concept of smart manufacturing, with the case of a motor manufacturing line. Explore the relationship between the project content in the finished motor data and all processes of pre-engineering, coils, and assembly. The objective of this study is to improve the efficiency of motor quality control inspection. The method is mainly to use expert interviews and secondary data to collect case company data. This study finds that: 1. This research is based on the initial 88 testing items, of which 14 items can be referenced in the motor manufacturing process. 2. This study proposes the relevant parts and production steps corresponding to these 14 items, which can evaluate the error points from the values of the motor finished product inspection items and simplify the time spent in quality control inspection. Finally, this study proposes practical implications and future research recommendations.

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Induction machine model with space harmonics for the diagnosis of rotor eccentricity, based on the convolution theorem

Cited by 16 publications

References 63 publications

Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Analytical Model of Induction Machines with Multiple Cage Faults Using the Winding Tensor Approach

The Investigation of Motor Production Test Indicators in Qualitative Research Methods

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