Stator and rotor faults detection in Induction Motor (IM) using the Extended Kaman Filter (EKF)

Rayyam, Marouane; Zazi, Malika; Hajji, Youssef; Chtouki, Ihssane

doi:10.1109/eitech.2016.7519579

Cited by 14 publications

(10 citation statements)

References 8 publications

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“…rotor resistance must be compensated for as part of this calculation procedure. In [10], a classification method is built using a multivariate relevance vector machine with Gaussian kernels and principal component analysis. The paper [11] presents that the pattern recognition and the Support Vector Machine (SVM) approach have recently been found to be surprisingly successful in a variety of real-world applications.…”

Section: Features and Stator Current Envelopementioning

confidence: 99%

“…The input matrix has 110 datasets and it was divided into 2 datasets, one for training (100) and one for testing (10), resulting in 20 training samples and 2 testing samples for 5 cases of induction motor, which include healthy, broken part of one bar, one broken bar, two broken bars and three broken bars. To test the Pattern Net's effectiveness and position for the given classification approach, different numbers of hidden layer neurons were used to train it.…”

Section: Neural Network-based Fault Diagnosismentioning

confidence: 99%

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Broken rotor bars fault detection in induction motors based on current envelope and neural network

Mustafa

2021

EEJET

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The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions

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Section: Features and Stator Current Envelopementioning

confidence: 99%

Section: Neural Network-based Fault Diagnosismentioning

confidence: 99%

Broken rotor bars fault detection in induction motors based on current envelope and neural network

Mustafa

2021

EEJET

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“…The discrete model of IM can be expressed in a deterministic state space representation using measured stator current and voltage (Rayyam et al, 2016). An extended IM model results if the rotor resistance is included as additional state variable (4)…”

Section: Extended Induction Motor Model For Broken Bar Fault Diagnosismentioning

confidence: 99%

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

Zazi

2019

Transactions of the Institute of Measurement and Control

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This paper introduces a novel metaheuristic model-based scheme for fault monitoring in squirrel cage induction motors (SCIMs). This method relies on the combination of the ant lion optimizer (ALO) and the unscented Kalman filter (UKF) to detect and quantify the number of broken bars. Contrary to the UKF-based fault diagnosis, the improved ALO-UKF algorithm tunes optimally and automatically the noise covariance matrices Q and R, which reduces the estimation errors, and then obtains an effective and accurate fault diagnosis. Firstly, a mathematical model of the fault under study has been developed based on rotor parameter value as signature. Secondly, a sixth order ALO-UKF algorithm has been synthesized for simultaneous estimation of rotor resistance and speed. Several broken bar fault conditions have been simulated. Simulation results show the effectiveness and robustness of the proposed ALO-UKF scheme in broken bar detection and identification, and exhibit a more superior performance than the simple-UKF and EKF algorithms in term of stability, accuracy and response time.

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“…The given estimation results of the proposed augmented EKF have fluctuations and also there is no result about the speed control. i sα , i sβ , R r , and R s are estimated with the required motor flux − αβ components by a single EKF in [31,32]. The proposed EKF‐based estimators are tested in Matlab/Simulink in simulation in these studies and m is obtained from the speed sensor for the dynamic control of the IM.…”

Section: Introductionmentioning

confidence: 99%

A novel FPGA‐Based Bi input‐reduced order extended Kalman filter for speed‐sensorless direct torque control of induction motor with constant switching frequency controller

İnan

2021

IET Computers & Digital Tech

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This study proposes an FPGA‐based hardware in the loop (HIL) emulator for speed‐sensorless of induction motor (IM) constant switching frequency controller‐based direct torque control (CSFC‐DTC) with a novel bi input‐reduced order extended Kalman filter (BI‐ROEKF). The full precision single floating point numbers in the IEEE 754 standard are used during the implementation of the HIL emulator which contains closed‐loop speed‐sensorless drive system of IM on the Xilinx Virtex XC5VLX‐110T ML506 FPGA board. In this HIL emulator of speed‐sensorless IM drive system, stator stationary axis components of stator flux, rotor mechanical angular speed, load torque, stator and rotor resistances are estimated with the novel BI‐ROEKF which is proposed for the first time in the literature. The proposed BI‐ROEKF is created by applying two different non‐linear and linear system input functions obtained from two different IM models to the single reduced order extended Kalman filter (ROEKF) algorithm. Thus, the order and the computational burden of the EKF are reduced. The HIL emulator of the speed‐sensorless drive system of IM is implemented on FPGA using the advantage of hand‐written VHDL on getting an optimal logical design to reduce the sampling time which directly effects the estimation performance of the model‐based estimator like the novel BI‐ROEKF and hence the control performance of drive system. The estimation performance of the novel BI‐ROEKF is tested with speed‐sensorless CSFC‐DTC IM drive system under different challenging scenarios in HIL emulator. Thus, the control and the implementation performances of digitalised emulator are tested. Finally, the estimation and control performance results and the execution time of the each part of the proposed HIL emulator of the speed‐sensorless BI‐ROEKF‐based CSFC‐DTC of the IM are presented.

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Stator and rotor faults detection in Induction Motor (IM) using the Extended Kaman Filter (EKF)

Cited by 14 publications

References 8 publications

Broken rotor bars fault detection in induction motors based on current envelope and neural network

Broken rotor bars fault detection in induction motors based on current envelope and neural network

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

A novel FPGA‐Based Bi input‐reduced order extended Kalman filter for speed‐sensorless direct torque control of induction motor with constant switching frequency controller

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