A novel metaheuristic model-based approach for accurate online broken bar fault diagnosis in induction motor using unscented Kalman filter and ant lion optimizer

Rayyam, Marouane; Zazi, Malika

doi:10.1177/0142331219892142

Cited by 17 publications

(5 citation statements)

References 25 publications

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“…It is often employed for the fault detection of nonlinear systems and offers estimations of the state and features of a dynamic system. [262,263] 5 Unscented Kalman Filter (UKF)…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Akbar,

Vaimann,

Asad

et al. 2023

Energies

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Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

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“…It is often employed for the fault detection of nonlinear systems and offers estimations of the state and features of a dynamic system. [262,263] 5 Unscented Kalman Filter (UKF)…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Akbar,

Vaimann,

Asad

et al. 2023

Energies

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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%

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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“…It is also well known that the control performance is dependent on accurate knowledge of the control variables/states that are generally obtained by using the observers instead of direct measurements leading to an increase in cost and hardware complexity. Moreover, the correct estimation and update of the parameters to both the control system and the observer itself results in enhanced performance for field-oriented control (Adamczyk and Orlowska-Kowalska, 2022), model predictive control (Zerdali and Demir, 2021), fault-tolerant control (Raisemche et al, 2016; Sobanski and Orlowska-Kowalska, 2017), and fault diagnosis (Namdar et al, 2022; Rayyam and Zazi, 2020). However, due to the frequency and temperature-dependent variations in rotor and stator resistance ( R r and R s , respectively), the flux level-dependent variation in inductances, and unknown mechanical parameters, it is still an open area to research for academic people and industries concentrating on the state and parameter estimation of IMs.…”

Section: Introductionmentioning

confidence: 99%

Online estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter

Yıldız

Demir

Barut

2023

Transactions of the Institute of Measurement and Control

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In this study, a novel extended Kalman filter (EKF)-based observer is designed to increase the number of estimated states and parameters of the induction motor (IM). To perform the online estimations of stationary axis components of stator currents and rotor fluxes ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) as well as rotor mechanical speed ([Formula: see text]), which are required for direct vector control (DVC) systems along with the load torque ([Formula: see text]), rotor resistance ([Formula: see text]), magnetizing inductance ([Formula: see text]), and the reciprocal of the total inertia of the system ([Formula: see text]), the proposed EKF uses the measured phase currents and voltages together with the measured rotor speed. To estimate all of the five states ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) plus four parameters ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]), the proposed EKF-based observer does not include a switching operation nor a hybrid structure, which is a common approach in the literature for online state and parameter estimations of IMs and results in design complexity and computational load increase. In simulation studies, the estimation performance of the proposed EKF is tested and verified under the variations of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] in DVC systems that perform the speed and position controls of IM. The obtained results confirm the satisfying tracking performances and thus better control achievements of the speed and position controlled IM drives in this paper. Moreover, the proposed EKF and the EKF without [Formula: see text]-estimation are compared in the position control system to demonstrate the importance of the [Formula: see text] estimation. In the comparison, nearly 10 times less mean square error (MSE) is obtained in the estimations of [Formula: see text], [Formula: see text], [Formula: see text], and the magnitude of the rotor flux for the proposed EKF. Finally, the proposed EKF algorithm is tested and verified in real-time experiments with a challenging speed reversal scenario causing nonlinear variations in both [Formula: see text] and [Formula: see text].

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A novel metaheuristic model-based approach for accurate online broken bar fault diagnosis in induction motor using unscented Kalman filter and ant lion optimizer

Cited by 17 publications

References 25 publications

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Advanced Signal Processing Methods for Condition Monitoring

Online estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter

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