Enhanced FFT-based method for incipient broken rotor bar detection in induction motors during the startup transient

Rivera-Guillen, Jesus Rooney; Santiago-Perez, J. Jesus de; Amezquita-Sanchez, Juan P.; Valtierra-Rodríguez, Martín; Romero‐Troncoso, Rene J.

doi:10.1016/j.measurement.2018.04.039

Cited by 47 publications

(30 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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%

“…The theoretical IF of the fault harmonics generated by a ME fault in the slip-frequency plane, applying Equation (2), consists only of two straight segments, with a slope equal to ± f 1 /p. In the case of the motor used in this section (Appendix B), which is a two pole pairs machine, p = 2, so Equation 2becomes (11) and the theoretical slope of the fault harmonics is ± f 1 /2.…”

Section: Theoretical If Of the Mixed Eccentricity Fault Related Harmomentioning

confidence: 99%

“…MCSA relies on the detection of the specific fault harmonics that each type of fault induces in the stator current, which may be considered their characteristic signature. In steady state, this signature can be detected in the spectrum of the stator current, obtained using the FFT [11][12][13][14][15]. Some of the most common induction machine faults and their characteristic signatures are:…”

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

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 86%

Section: Theoretical If Of the Mixed Eccentricity Fault Related Harmomentioning

confidence: 99%

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

“…To extend MCSA to the fault diagnosis of IM working in transient regime, advanced time-frequency (TF) transforms of the current are needed, so that the transient fault signatures can be identified in a joint TF domain. These transforms can be linear, such as the short-time Fourier transform (STFT) [28][29][30][31], the short-frequency Fourier transform (SFFT) [31,32] and the wavelet transform (WT) [33], or quadratic, such as the Wigner-Ville distribution (WVD) [34] or the ambiguity function [8]. Quadratic TF transforms can achieve optimal resolution for mono-component chirp signals but, in case of multi-component ones, they produce cross-terms artifacts that pollute the TF representation of the current, making it difficult the correct identification of the fault harmonics.…”

Section: Type Of Fault Fault Harmonics Frequencymentioning

confidence: 99%

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

et al. 2019

View full text Add to dashboard Cite

Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain-such as a spectrogram-is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it-short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

show abstract

“…Several sensing techniques have been researched in the field of BRB fault diagnosis. For example, motor current signature analysis (MCSA) [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] is the mainstream technique, and the motor vibration signature analysis (MVSA) [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] technique is commonly used for diagnosing the presence of BRB failures. However, the sound measurement [32], temperature measurement [33], and magnetic field analysis [34] have been used less due to the inference of external factors.…”

Section: Introductionmentioning

confidence: 99%

An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors

Zhen

Wang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.

show abstract

Enhanced FFT-based method for incipient broken rotor bar detection in induction motors during the startup transient

Cited by 47 publications

References 35 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

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors

Contact Info

Product

Resources

About