Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients

Garcia-Calva, Tomas Alberto; Moriñigo-Sotelo, Daniel; Fernandez-Cavero, Vanessa; García-Pérez, Arturo; Romero-Troncoso, René de Jesús

doi:10.3390/en14051469

Cited by 23 publications

(20 citation statements)

References 39 publications

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“…To create this fault condition, first, we aligned both the driver and the driven SCIM with a precision alignment tool, and then we created a misalignment of 0.3 mm for both angular and parallel rotor misalignment to achieve AMT-1. The broken rotor bar fault often results from an accumulation of mechanical stress springing from various operating conditions [36]. To achieve this, the BSC-2 was created by boring two holes each with a diameter and depth of 5 mm on the rotor bar of an SCIM.…”

Section: Bsc-2mentioning

confidence: 99%

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

Okwuosa

Hur

2022

Energies

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In most manufacturing industries, squirrel cage induction motors (SCIMs) are essential due to their robust nature, high torque generation, and low maintenance costs, so their failure often times affects productivity, profitability, reliability, etc. While various research studies presented techniques for addressing most of these machines’ prevailing issues, fault detection in cases of low slip or, low load, and no loading conditions for motor current signature analysis still remains a great concern. When compared to the impact on the machine at full load conditions, fault detection at low load conditions helps mitigate the impact of the damage on SCIM and reduces maintenance costs. Using stator current data from the SCIM’s direct online starter method, this study presents a feature engineering-aided fault classification method for SCIM at minor-load conditions based on a filter approach using the support vector classification (SVC) algorithm as the classifier. This method leverages the loop-hole of the Fourier Transform at minor-load conditions by harnessing the uniqueness of the Hilbert Transform (HT) to present a methodology that combines different feature engineering technologies to excite, extract, and select 10 discriminant information using a filter-based approach as the selection tool for fault classification. With the selected features, the SVC performed exceptionally well, with a significant diagnostic performance accuracy of 97.32%. Further testing with other well-known robust classifiers such as decision tree (DT), random forest (RF), k-nearest neighbor (KNN), gradient boost classifier (GBC), stochastic gradient descent (SGD), and global assessment metrics revealed that the SVC is reliable in terms of accuracy and computation speeds.

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Section: Bsc-2mentioning

confidence: 99%

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

Okwuosa

Hur

2022

Energies

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“…Defects appearing at the initial stage does not lead to emergency operation modes and a shutdown of the machine. Such failures can include: (i) failures and interturn short circuits of stator winding (Bento et al, 2021;Pietrzak & Wolkiewicz, 2021); (ii) failure of active steel package integrity (Ostroverkhov & Nalivaiko, 2020); (iii) rotor bars breakage (Garcia-Calva et al, 2021;Fernandez-Cavero et al, 2021); (iv) mechanical loosening of fasteners and couplings (Xiao et al, 2022).…”

Section: Induction Motors Deterioration and Defectsmentioning

confidence: 99%

Analysis of the behavior of asynchronous electric drive with a closed scalar control system when changing the inductance of the magnetizing circuit

Zhukovskiy

Vasilev

Korolev

et al. 2022

Indonesian J. Sci. Technol

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The article was devoted to the study of an automated electric drive with a scalar closed-loop speed control system. Severe duty and operating modes of electric drive determine the actual service life. Wear of the induction motor, as a key link of the electric drive, was associated with deviation from nominal parameters. The deviation of parameters of the induction motor equivalent circuit determined the resultant change of characteristics. The parameters of the equivalent circuit determined the accuracy of the adjustment of regulators and optimal algorithms in the control system of the electric drive. In continuous operation modes the possibility of auto-tuning regulators, which requires stopping or no-load mode, was excluded. The paper considered the influence of the magnetization circuit mutual inductance value of the induction motor on the behavior of the electric drive control system. Evaluation of the behavior of the scalar closed-loop speed control system was performed on the basic energy (power factor, efficiency factor) and mechanical (speed, electromagnetic torque) characteristics of the electric drive.

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“…can come in handy for fault detection at an early stage. Several studies in the literature describe rotor and statorrelated fault isolation and prognostics using motor's electrical parameter analysis [13,14]. Thus, modeling an RUL framework using the electrical parameters will provide a robust framework for dynamic operating conditions.…”

Section: State-of-the-artmentioning

confidence: 99%

A Data Driven RUL Estimation Framework of Electric Motor Using Deep Electrical Feature Learning from Current Harmonics and Apparent Power

2021

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An effective remaining useful life (RUL) estimation method is of great concern in industrial machinery to ensure system reliability and reduce the risk of unexpected failures. Anticipation of an electric motor’s future state can improve the yield of a system and warrant the reuse of the industrial asset. In this paper, we present an effective RUL estimation framework of brushless DC (BLDC) motor using third harmonic analysis and output apparent power monitoring. In this work, the mechanical output of the BLDC motor is monitored through a coupled generator. To emphasize the total power generation, we have analyzed the trend of apparent power, which preserves the characteristics of real power and reactive power in an AC power system. A normalized modal current (NMC) is used to extract the current features from the BLDC motor. Fault characteristics of motor current and generator power are fused using a Kalman filter to estimate the RUL. Degradation patterns for the BLDC motor have been monitored for three different scenarios and for future predictions, an attention layer optimized bidirectional long short-term memory (ABLSTM) neural network model is trained. ABLSTM model’s performance is evaluated based on several metrics and compared with other state-of-the-art deep learning models.

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Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients

Cited by 23 publications

References 39 publications

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

Analysis of the behavior of asynchronous electric drive with a closed scalar control system when changing the inductance of the magnetizing circuit

A Data Driven RUL Estimation Framework of Electric Motor Using Deep Electrical Feature Learning from Current Harmonics and Apparent Power

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