Air‐gap eccentricity fault diagnosis and estimation in induction motors using unscented Kalman filter

Bagheri, Ahmad; Ojaghi, Mansour; Bagheri, Amir

doi:10.1002/2050-7038.12450

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…The elements of covariance matrices serve as parameters to influence the convergence of the KF algorithm. There are many modified versions of KF such as extended KF, unscented KF, switching KF and others [11,28,78].…”

Section: Kalman Filtermentioning

confidence: 99%

“…Interestingly, the presented approach shows a huge decrease in relative accuracy at near failure moments, which were caused by strong bearings vibrations during approaching failure states. Bagheri et al [11] used KF to detect degrees of the static, dynamic and mixed eccentricity faults of IMs. Different levels of eccentricities simulated using an 11 kW three-phase IM were used for experiments (stator current and voltage signals).…”

Section: Kalman Filtermentioning

confidence: 99%

See 1 more Smart Citation

Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

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Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park’s Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.

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Section: Kalman Filtermentioning

confidence: 99%

Section: Kalman Filtermentioning

confidence: 99%

Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Aiming at fault estimation for suspension systems of HSTs, a Kalman filter-like fault estimation algorithm (Liu et al, 2016) and an improved Kalman filter (Kim et al, 2020) have been proposed. For fault detection and fault estimation of the traction motor of HST, nonlinear Kalman filters have been proposed (Ameid et al, 2017; Bagheri et al, 2020). Nevertheless, these Kalman filtering methods remain at the level of fault diagnosis of the structural components but not the dynamics of HST.…”

Section: Introductionmentioning

confidence: 99%

Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended Kalman filter

Liang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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In this paper, an optimized adaptive robust extended Kalman filter is proposed based on random weighting factors and an improved whale optimization algorithm for fault estimation of the dynamics of high-speed trains with constant time delays, drastically changing noise and stochastic uncertainties. Robust upper bounds are proposed to improve the performance of the extended Kalman filter by decreasing the influence of the linearization error on filtering for the dynamics of high-speed trains with constant time delays, and its robustness is proven to guarantee the feasibility of the proposed upper bounds. Furthermore, considering drastically changing noise with unknown statistics, a random weighting adaptive algorithm is proposed to implement unbiased noise estimation so that the robust extended Kalman filter can still be implemented well. In addition, a differential evolution algorithm and adaptive parameter are introduced to improve the performance of the whale optimization algorithm so that the stochastic uncertainties are optimized, and the influence of the stochastic uncertainties on filtering is further decreased. The simulation results in the three conditions show that, compared with the variational Bayes adaptive iterated extended Kalman filter, using the proposed method, the position, speed and fault estimation errors are decreased by 31.8%, 33.2% and 28.3%, respectively, on average, which depends on more accurate noise estimation.

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“…However, the introduced observer is sensitive to the variations caused by temperature and frequency changes and should be updated continuously. Bagheri [26] introduces a new independent approach of external factors for eccentricity fault detection and discrimination in induction motors and estimates the exact severities of the faulty components using an unscented Kalman-Bucy filter (UKBF), which shows efficient performance in different types of eccentricity faults. Ortatepe and Karaarslan [27] have proposed the employment of a reduced-order EKF instead of the full-order EKF in a DFIG-based wind turbine to increase the system stability against variations of rotor and stator resistors.…”

Section: Introductionmentioning

confidence: 99%

MPPT Improvement for PMSG-Based Wind Turbines Using Extended Kalman Filter and Fuzzy Control System

et al. 2021

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Variable speed wind turbines are commonly used as wind power generation systems because of their lower maintenance cost and flexible speed control. The optimum output power for a wind turbine can be extracted using maximum power point tracking (MPPT) strategies. However, unpredictable parameters, such as wind speed and air density could affect the accuracy of the MPPT methods, especially during the wind speed small oscillations. In this paper, in a permanent magnet synchronous generator (PMSG), the MPPT is implemented by determining the uncertainty of the unpredictable parameters using the extended Kalman filter (EKF). Also, the generator speed is controlled by employing a fuzzy logic control (FLC) system. This study aims at minimizing the effects of unpredictable parameters on the MPPT of the PMSG system. The simulation results represent an improvement in MPPT accuracy and output power efficiency.

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Air‐gap eccentricity fault diagnosis and estimation in induction motors using unscented Kalman filter

Cited by 13 publications

References 25 publications

Advanced Signal Processing Methods for Condition Monitoring

Advanced Signal Processing Methods for Condition Monitoring

Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended Kalman filter

MPPT Improvement for PMSG-Based Wind Turbines Using Extended Kalman Filter and Fuzzy Control System

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