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Hiba Ziad Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

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Ayad Al-dujaili Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

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Amjad J. Humaidi Control and Systems Engineering Department, University of Technology, Baghdad, Iraq

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Abstract

The predictive maintenance of permeant magnet synchronous motor is highly required as this kind of motor has been commonly employed in electric vehicles, industrial systems, and other applications owing to its high power density output, as well as the regenerative operation characteristics during braking and deceleration driving conditions. One of the most important causes of PMSM failure is the stator short and drive switches failure. These problems have attracted more attention in the field of deep learning for fault detection purposes in the early stages, to avoid any system breakdown, and to decrease the risk and price of maintenance. In this paper, we investigate the possibility of detecting the electrical faults in PMSM by generating our data which includes current signals that have been analyzed and preprocessed by applying Continuous Wavelet Transform (CWT) to select the reliable features this conversion will be used to train ResNet 50. The evaluation metrics have shown that ResNet 50 achieves an accuracy of 100% for the classification of faults.

Abstract

The predictive maintenance of permeant magnet synchronous motor is highly required as this kind of motor has been commonly employed in electric vehicles, industrial systems, and other applications owing to its high power density output, as well as the regenerative operation characteristics during braking and deceleration driving conditions. One of the most important causes of PMSM failure is the stator short and drive switches failure. These problems have attracted more attention in the field of deep learning for fault detection purposes in the early stages, to avoid any system breakdown, and to decrease the risk and price of maintenance. In this paper, we investigate the possibility of detecting the electrical faults in PMSM by generating our data which includes current signals that have been analyzed and preprocessed by applying Continuous Wavelet Transform (CWT) to select the reliable features this conversion will be used to train ResNet 50. The evaluation metrics have shown that ResNet 50 achieves an accuracy of 100% for the classification of faults.

1 Introduction

PMSMs are a type of motor that exhibits superior dynamic performance and exceptional dependability. Because PMSM motors are utilized to drive a range of loads, they are an invaluable tool in the transportation, aerospace, and industrial automation sectors. Because the permanent magnet synchronous motor does not require slip rings for field excitation, it requires less maintenance and has lower rotor losses. Because of their great efficiency, PMSMs are suitable for high-performance drive systems found in the industry, for instance CNC machines and robotic and autonomous production systems [1]. There are three types of failures in PMSMs: mechanical, electrical, and magnetic. The creation and dispersion of magnetic field lines are connected to magnetic faults. Stator faults and external problems (connected to drives) are two other categories of electrical defects. The majority of insulation-based failures occur in the winding section of the stator. Typically, insulation failure is the cause of these defects. Fault prediction in PMSMs is required to improve their performance, lengthen their lifespan, and reduce their expensive costs [2].

Electric motor fault recognition and evaluation have been thoroughly studied to date such as the method that depends on knowledge, signal, model, and combinations of these methods. A model-based system requires a precise computational model, on the other hand, a signal-based system is highly reliant on the operation point of the machine [3, 4]. Conversely, knowledge-based categorization arranges the fault types using a large number of experimental datasets. Therefore, data-driven approaches are appropriate for specific signal sequences or highly nonlinear structures with an unknown model [5]. The idea is basically to train the Deep Learning Network with data from both healthy and malfunctioning motors to eventually distinguish between the two and have the capacity to warn when a motor is about to break down before it happens.

In the last decade, Artificial Intelligence has been wildly developed and has been employed for fault detection in electrical motors. In the past few years, a lot of researchers paid attention to the detection and classification of PMSM fault by employing machine learning (ML) techniques such as the k-nearest neighbor (KNN) algorithm [6], generative adversarial networks [7], support vector machines (SVM) [8], artificial neural networks [9], and linear regression [10].

Abed et al. [10] used the Multilayer Perceptron (MLP) feed-forward neural network to classify the inter-turn short circuit issue in the PMSM stator winding. In [11], Chen et al. created the Autoregressive of the Nonlinear Utilizing Exogenous Model (NARX), a series of techniques controlled to determine the degree of fault in an open circuit emerging through a toggle switch in aircraft PMSM.

Over the past 20 years, deep learning (DL) has been shown to be an extremely effective method for fault prediction in a variety of industries [12–16], showcasing its capacity to automatically recognize characteristics from raw data. As a result, it can reduce reliance on human experts' diagnostic expertise (meaningful feature extraction and selection). Furthermore, by employing a multilayer design, DL can establish a connection between the type of failure and the experimental data. Numerous deep learning (DL) approaches, including convolution neural networks (CNN) [17] and deep belief networks (DBNs) [18], have already been employed for fault identification. CNN is widely used in defect identification due to its capacity to extract meaningful hierarchical features [17]. The time-domain signals are the most widely accessible data. Moreover, the development of a massive number of defective machine data is laborious in the FDI sector, and DL approaches rely on a substantial amount of data to maximize the category masses for predictions. Furthermore, when dealing with limited datasets that have a higher number of trainable issues, deep learning networks also tend to overfit. Hence, before being used in the FDI area, DL techniques require special architectural enhancements.

The motor data generated and labeled manually from the simulation is limited and contains a variation in the period domain especially in faulty cases. The raw signal in time series of the stator current is converted to the time-frequency domain via employing continuous wavelet transform (CWT) which allows the extraction of reliable features simply without losing any information in the original signal. These limitations inspired us to investigate the residual network (ResNet) ability to classify the sudden changes in the frequency when a malfunction occurs without using transfer learning and to study the impact of training factors on the category results such as batch size, learning rate, and data division. This will reduce the probability of the model to be overfitted.

This research aims to develop a classification method for PMSM stator current where the measured stator current does not require monitor devices or complicated signal proceeding techniques. Furthermore, the CWT-based scalogram can extract high-order features from the original signal that will improve the accuracy percentage of the classification system. Also, the use of ResNet has reduced the need for human intervention and expertise in feature extraction and selection.

The paper is arranged into five sections. The introduction which is presented in the first section followed by the second section which presents the principle of proposed work that starts with the simulation of PMSM and faults generation, data acquisition and transformation, data augmentation, and ends with the training of the ResNet structure. In the third section, the experimental results are presented in details and it is followed by discussion in the fourth section. Finally, the summary of the proposed method and the future work are introduced in section five.

2 Fault classification method

The experiment is completely based on diagnosing faults in a PMSM under various operating scenarios using frequency-domain properties. When the motor operates under various conditions, the fundamental frequency of the stator current varies, resulting in a variation in the fault frequency. As this study focuses on the classification of electrical faults which includes driving failures and stator winding short-turn faults are frequent; the most obvious kind is inter-turn short-circuit. Electrical malfunctions can generally be classified as connection errors, ground errors, stator phase winding short circuits, and full phase open circuits. The most prevalent of those defects is known as the Inter-Turn Short Fault (ITSF), and this research will primarily examine it. At each simulation, the measured current signal will be processed by applying CWT and converted to an image that carries the best features in the original signal. After establishing the faulty and healthy feature library, three types of ResNet networks will be trained for classification purposes as shown in Fig. 1.

Fig. 1.
Fig. 1.

Process for diagnosing faults

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2024.00789

2.1 PMSM Simulink model and data acquisition

The characteristics of permanent magnet synchronous motor include current, voltage, torque, magnetic flux, and vibration, can vary due to any type of defect. However, the information carried by the phase current signal is more useful. As seen in “Fig. 2” which demonstrates the PMSM simulation model used to gather phase current signals through the use of the Sim Power system toolbox in the Matlab/Simulink environment. To provide an AC output with a changeable frequency, the inverter modifies the input DC voltage. In PMSM there are two modes of operation, generator or motor, and this can be done by the mechanical torque's ‘T’ sign (negative sign for generator mode, positive sign for motor mode). It is possible to modify the speed in a closed loop through determining the real rotor speed and comparing it to the reference speed. The inputs to the PMSM block are the three-phase inverter, load torque, and terminal phase voltages.

Fig. 2.
Fig. 2.

Simulation of PMSM and signal collection

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2024.00789

To generate our data, the PMSM was experimentally tested under standard operation. The specification of the motor is described in Table 1. The stable speed of the motor indicates that the motor is running under normal operation, and that the current phase signal has no sudden changes in the amplitude. The experimental model shown in Fig. 2 was composed of a three-phase PMSM fed by a leg inverter supplied by a DC voltage source 500 V. The phase of ‘B’ measured current signal is represented in Fig. 3 as a sample of time series current signal of the motor under healthy operation.

Table 1.

Three-phase PMSM mechanical and electrical parameters

ParameterValueUnit
The resistance of stator, Rs2.8
Leakage inductance of stator, Lis0.42H
Flux linkage, λ0.175Wb
Rated speed, Wm3,000RPM
Moment of inertia, J0.03Kg.m2
No. of poles3
Fig. 3.
Fig. 3.

Represents the phase ‘B’ current signal under normal operation

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2024.00789

Now to introduce the fault signals, the PMSM was emulated in different scenarios and a lookup table (see Table 2) was made to introduce both open and short circuit faults for the drive side. For motor faults, coil-coil fault and coil-to-ground fault have been added and the fault was injected during the simulation after 0.2 s of operation. The fault resistance value was set to a minimum till the sudden change occurred in the phase current signal.

Table 2.

Lookup table of the fault generated from the inverter side

Fault typePhaseS1S2S3S4S5S6
Open circuit‘A’OFFONONONONON
‘A’ONONONOFFONON
‘B’ONONOFFONONON
‘B’ONONONONONOFF
‘C’ONONONONOFFON
‘C’ONOFFONONONON
‘A’, ‘C’OFFOFFONONONON
‘A’, ‘B’OFFONONONONOFF
‘A’, ‘B’ONONOFFOFFONON
‘B’, ‘C’ONOFFOFFONONON
‘A’, ‘C’ONONONOFFOFFON
‘B’, ‘C’ONONONONOFFOFF
‘A’, ‘B’OFFONOFFONONON
‘B, ‘C’ONONOFFONOFFON
‘A’, ‘C’OFFONONONOFFON
‘A’, ‘C’ONONONOFFONOFF
‘B’, ‘C’ONOFFONONONOFF
‘A’, ‘B’ONOFFONOFFONON
‘A’, ‘B’, ‘C’OFFONOFFONOFFON
‘A’, ‘B’, ‘C’ONOFFONOFFONOFF
‘A’, ‘B’, ‘C’OFFONONONOFFOFF
‘A’, ‘B’, ‘C’ONOFFOFFOFFONON
‘A’, ‘B’, ‘C’OFFOFFONOFFONOFF
‘A’, ‘B’, ‘C’ONOFFOFFOFFONOFF
‘A’, ‘B’, ‘C’ONOFFONOFFOFFOFF
‘A’, ‘B’, ‘C’OFFONOFFOFFOFFON
‘A’, ‘B’, ‘C’OFFONOFFONOFFOFF
‘A’, ‘B’, ‘C’OFFOFFOFFONOFFON

Additionally, open circuit faults in a single or multiple phase can be achieved by removing the IGBT of phase leg ‘X’ before motor simulation and the same steps were repeated for multiple phases of open circuit fault as described in Table 2, and illustrated in Fig. 4 as sample of time series of the measured current signal when the motor operated under unhealthy condition. However, to generate a phase short circuit, we repeated the same lookup table but in this case, we manipulated several IGBT parameters for the leg phase such as the internal resistance, the forward voltage of the IGBT device, the snubber resistance (this was set to minimum or eliminated), and the snubber capacitance.

Fig. 4.
Fig. 4.

Illustrates the phase ‘B’ current signal under abnormal operation

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2024.00789

2.2 Data transformation and augmentation

The phase current signal in the time domain was gathered and analyzed at various lengths that can be transformed into the frequency domain using the continuous wavelet transform (CWT), which uses the inner products to calculate the similarity of a signal to the function of analysis. A simple illustration of this idea is shown in Fig. 5.

Fig. 5.
Fig. 5.

Demonstrate data processing where (a) represents the raw data (time-domain signal), (b) the scalogram image obtained from the absolute value of the CWT, and (c) illustrates the final image which is 2D image of size 224*224*3

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2024.00789

The absolute value of the CWT is called the scalogram image and it can be obtained from the time scale spectrum and amplitude produced by the CWT. Then, the 2D image can be achieved by recalculating the filter bank number which depends on the length of the time series signal [18].

There are 526 labels in the dataset, one for every class. Even when the dataset already has extracted characteristics, pretreatment is still necessary to prevent overfitting and to greatly enhance the efficiency of deep learning models during training. However, the system behaves subparly when detecting the minority class due to bias across the presiding class. To prevent that, data balancing is carried out. Additionally, a modified version of the original dataset has been added to the quantity through the use of methods for data enhancement. On the training dataset alone, we used five different kinds of data augmentation techniques, including random rotation, vertical cropping, horizontal cropping, and reflection as techniques contributing to raising the accuracy of the model, in addition to the random chosen of the training dataset at each epoch.

2.3 Training setup

In the present research, a 224 × 224 pre-processed RGB image is fed into the neural network, which divides it into two classes: Healthy and Faulty. Since the suggested ImageNet-based pre-trained ResNet model was applied, our data had to be utilized to develop the dense and dropout levels of the classification layers from scratch. However, the convolution layer of the ResNet model was run on our training dataset, generating the final layer output vectors. The Soft max classifier is used as a classification layer which will train on the output vectors of the final layer. The experiment is implemented by using a Matlab environment with a single CPU. The training dataset was randomly chosen from the original dataset at each training process and the division of training data was started with 60%, 70%, and 80% respectively. The number of epochs was set as 10. The Adaptive Moment Estimation (Adam) algorithm was used and the learning rate was used as 0.1, 0.01, 0.01, 0.001, and 0.0001 respectively. The batch size during training was started as 32, 16, and 8, respectively.

3 Experimental results

To reflect the general efficacy of a classification model when examined on a set of test data, the confusion matrix is used to combine the true positive, true negative, false positive, and false negative in one table for each labelled class. Confusion matrix is built by comparing the observed class labels in the test dataset to the predicted class labels given by the model. Rows indicate the actual class labels, whereas columns show the expected class labels. However, the experiment results of the proposed method were evaluated by checking the variations between the real categories and the classifications produced by the classifier, and this process is called the accuracy which can be calculated as follows:
ACC=TP+TNTP+TN+FN+FP
where true positive refers to the model which correctly predicted the positive cases and it is symbolized by TP. Correspondingly, true negative refers to the model that correctly identified negative cases and it is denoted by TN. Additionally, the false positive represents wrong model's prediction number of positive classes and it is denoted by FP, while the FN refers to the model which incorrectly predicted the negative cases. Furthermore, the Recall metric evaluates how well the model can detect every positive case. It can be computed by dividing the total number of actual positive forecasts using the number of true positive predictions produced by the model:
Recall=TPTP+FN
The ratio of TP forecasts to all positive predictions is called precision which evaluates the model's potential to identify affirmative cases and it can be calculated as follows:
Precision=TPTP+FP
Last but not least, the system's ability to balance the harmonic mean of precision and recall is called the F1 score and it can be calculated as follows:
F1Score=2*TP2*TP+FN+FP

The experimental results shows that the ResNet 18, which has a lowest number of layers compared to other models can classify both faulty and healthy images when trained with 80% of training dataset, and setting the batch size to 16, and learning rate 0.0001. It achieves accuracy of 98.9% as shown in Table 3, which represents the number of attempts to get the best classification performance that is presented in Fig. 6.

Table 3.

Results of ResNet 18

NetworkData Div.Mini batchLearn rateMetrics
ACCF1RecallPrecision
ResNet 1860%320.182.979.410065.9
0.0178.774.393.561.7
0.00195.795.694.696.7
0.000196.396.110092.5
160.175.567.610051
0.019291.893.490.4
0.00194.193.898.889.3
0.000195.795.697.793.6
80.18381.887.876.5
0.0195.795.697.793.6
0.00191.494.288.294.3
0.000191.591.888.295.7
70%320.177.570.910054.9
0.0188.787.310077.4
0.00197.297.198.595.7
0.000199.399.210098.5
160.18887.492.183
0.0193.793.398.488.7
0.00193.793.79394.3
0.000195.195.391.1100
80.180.376.295.763.3
0.019392.696.988.7
0.00195.194.810090.1
0.000195.194.810090.1
80%320.18383.680.387.2
0.0193.693.110087.2
0.00197.897.810095.7
0.000197.897.897.897.8
160.179.875.396.661.7
0.0190.489.410080.8
0.00190.491.283.9100
0.000198.998.910097.8
80.150000
0.0190.489.410080.8
0.00196.896.895.897.8
0.000198.998.910097.8
Fig. 6.
Fig. 6.

Confusion matrix of ResNet 18

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2024.00789

Furthermore, the experimental results of ResNet 50 shows that this model achieves accuracy of 100% when trained with 80% of training dataset, and setting the batch size to 8, and learning rate 0.0001 as shown in Table 4, which introduced the number of attempts to get the best classification performance and that is illustrated in Fig. 7.

Table 4.

Results of ResNet 50

NetworkData Div.Mini batchLearn rateMetrics
ACCF1RecallPrecision
ResNet 5060%320.175.567.610051
0.0188.887.410077
0.00193.693.495.591.4
0.000197.397.210094.6
160.174.466.69651
0.0181.377.995.365.9
0.00193.696.796.590.4
0.000197.397.397.896.8
80.157.380.79371.2
0.0178.173.294.959.5
0.00183.585.476.496.8
0.000193.693.397.689.3
70%320.166.248.910032.3
0.01766997.453.5
0.00194.394.493.195.7
0.000198.598.510097.1
160.176.769.710053.5
0.0181.677.997.864.7
0.00192.993.190.695.7
0.000194.49410088.7
80.177.579.772.488.7
0.0167.65210035.2
0.0018886.410076
0.000194.494100100
80%320.171.359.710042.5
0.0178.778.778.778.7
0.00195.795.697.793.6
0.000198.998.910097.8
160.178.773.696.559.5
0.0181.97994.168
0.00194.694.310089.3
0.000198.998.910097.8
80.1506650100
0.01838096.968
0.00193.693.495.591.4
0.0001100100100100
Fig. 7.
Fig. 7.

Confusion matrix of ResNet 50

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2024.00789

For ResNet 101, the experimental results shows that this model achieves accuracy of 98.6% when trained with 70% of training dataset, and setting the batch size to 16, and learning rate 0.0001 as shown in Table 5 which introduced the number of attempts to get the best classification performance and that is illustrated in Fig. 8.

Table 5.

Results of ResNet 101

NetworkData Div.Mini batchLearn rateMetrics
ACCF1RecallPrecision
ResNet 10160%320.177.77296.457.4
0.016651.189.436.1
0.00189.990.187.892.5
0.000196.296.295.796.8
160.167.674.661.295.7
0.0181.982.679.486.1
0.00190.489.598.781.9
0.000198.998.910097.8
80.17578.369.190.4
0.0189.488.298.679.7
0.00185.68683.888.2
0.000193.193.489.397.8
70%320.176.171.687.760.5
0.0185.983.610071.8
0.00192.391.995.488.7
0.000198.698.697.2100
160.159.230.910018.3
0.0172.565.48852.1
0.00195.194.898.491.5
0.000198.698.698.698.6
80.159.937.38523
0.0164.163.864.263.3
0.00162.747.58033.8
0.000199.399.210098.5
80%320.187.286.392.680.8
0.0173.469.182.259.5
0.00192.692.794.797.8
0.000195.795.510091.5
160.173.463.710046.8
0.0162.849.277.236.1
0.00193.693.110087.2
0.000197.997.810095.7
80.154.333.861.123.4
0.0177.771.210055.3
0.00187.285.310074.4
0.000193.693.495.591.4
Fig. 8.
Fig. 8.

Confusion matrix of ResNet 101

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2024.00789

4 Discussion

The assessed models generally had good accuracy, ranging from 93.6 to 100%. All models reached values greater than 93% in the recall ratings, which were likewise quite high. The confusion matrices in Figs 57 show that ResNet 50 behaves better than other structures even when trained with a low data limit, and it can detect sudden changes in faulty signals. Moreover, it achieves the best accuracy of 100% when trained with 80% of the training dataset, setting the batch size to 8, and the learning rate to 0.0001. This tuning improves the results of ResNet 50 which can be observed in accuracy, recall, precision, and F1 score as shown in Table 6. From the results of recall and precision, it can be noticed that both ResNet 18 and ResNet 101 have been overfitted, so they misclassified some malfunctions cases.

Table 6.

Best results of all networks

NetMetrics
ACCF1 ScoreRecallPrecision
ResNet 1898.998.910097.8
ResNet 50100100100100
ResNet 10198.698.698.698.6

The results of ResNet 50 cast a new light on the possibility of employing this network for online electrical fault detection. It facilitates remote monitoring of the motor performance with the ability to predict the type of malfunction and to take appropriate action before it occurs. However, it is very possible to reduce the risks that may affect the motor, which may cause a certain production line to stop, or to avoid damage to other parts.

5 Conclusion

This paper focused on developing an automated method to accurately classify the electrical faults that occur in the PMSM. The complexities of using the signals which require more processing techniques and more time for training the network, signal to image approach is applied. However, the purpose of the research was to look into ResNet's potential for maintenance planning. The experimental results demonstrated that ResNet 50 achieved accurate fault classification when trained from scratch on our data which is limited and complicated in structure, however, the network achieved the state-of-the-art of 100% accuracy, recall, precision, and F1 score respectively. The evaluation metrics also show that the proposed network results are enhanced when fine-tuning is applied.

In the future work, one may suggest that other types of pre-trained networks can be used and compared to the proposed method [19–24]. Furthermore, other kinds of motors will be considered and added to our data to create more variety in the dataset. One can extend this study to include various control schemes for the motor under consideration taking into account the faults deduced from this study to present a fault controlled for PMSM based on ResNet Neural Network [25–39].

References

  • [1]

    A. J. Humaidi and A. H. Hameed, “PMLSM position control based on continuous projection adaptive sliding mode controller,” Syst. Sci. Control Eng., vol. 6, no. 3, 2018. https://doi.org/10.1080/21642583.2018.1547887.

    • Search Google Scholar
    • Export Citation
  • [2]

    B. Aubert, J. Régnier, S. Caux, and D. Alejo, “Kalman-filter-based indicator for online interturn short circuits detection in permanent-magnet synchronous generators,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 19211930, March 2015. https://doi.org/10.1109/TIE.2014.2348934.

    • Search Google Scholar
    • Export Citation
  • [3]

    Z. Gao, C. Cecati, and S. X. Ding, “A survey of fault diagnosis and fault-tolerant techniques—Part I: fault diagnosis with model-based and signal-based approaches,” IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 37573767, June 2015. https://doi.org/10.1109/TIE.2015.2417501.

    • Search Google Scholar
    • Export Citation
  • [4]

    X. Dai and Z. Gao, “From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis,” IEEE Trans. Ind. Inform., vol. 9, no. 4, pp. 22262238, Nov. 2013. https://doi.org/10.1109/TII.2013.2243743.

    • Search Google Scholar
    • Export Citation
  • [5]

    C. G. Dias and F. H. Pereira, “Broken rotor bars detection in induction motors running at very low slip using a hall effect sensor,” IEEE Sensors J., vol. 18, no. 11, pp. 46024613, 1 June, 2018. https://doi.org/10.1109/JSEN.2018.2827204.

    • Search Google Scholar
    • Export Citation
  • [6]

    H. Liu, J. Zhou, Y. Xu, Y. Zheng, X. Peng, and W. Jiang, “Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks,” Neurocomputing, vol. 315, pp. 412424, Nov. 2018. https://doi.org/10.1016/j.neucom.2018.07.034.

    • Search Google Scholar
    • Export Citation
  • [7]

    M. Altobi, G. Bevan, P. Wallace, D. Harrison, and K. Ramachandran, “Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT,” Eng. Sci. Technol. Int. J., vol. 22, no. 3, pp. 854861, Jun. 2019. https://doi.org/10.1016/j.jestch.2019.01.005.

    • Search Google Scholar
    • Export Citation
  • [8]

    S. Moosavi, A. Djerdir, Y. Amirat, and D. Khaburi, “ANN-based fault diagnosis of permanent magnet synchronous motor under stator winding shorted turn,” Electric Power Syst. Res., vol. 125, pp. 6782, Aug. 2015. https://doi.org/10.1016/j.epsr.2015.03.024.

    • Search Google Scholar
    • Export Citation
  • [9]

    W. Ahmad, S. A. Khan, and J.-M. Kim, “A hybrid prognostics technique for rolling element bearings using adaptive predictive models,” IEEE Trans. Ind. Electron., vol. 65, no. 2, pp. 15771584, Feb. 2018. https://doi.org/10.1109/TIE.2017.2733487.

    • Search Google Scholar
    • Export Citation
  • [10]

    W. Abed, Q. Aish, and A. Hamed, “Performance monitoring of aircraft PMSM based on soft computing technique,” Periodicals Eng. Nat. Sci. (Pen), vol. 9, no. 1, p. 293, 2021. https://doi.org/10.21533/pen.v9i1.1797.

    • Search Google Scholar
    • Export Citation
  • [11]

    Y. Chen, S. Liang, W. Li, H. Liang, and C. Wang, “Faults and diagnosis methods of permanent magnet synchronous motors,” Appl. Sci., vol. 9, no. 10, p. 2116, May. 2019. https://doi.org/10.3390/app9102116.

    • Search Google Scholar
    • Export Citation
  • [12]

    J. Sun, C. Yan, and J. Wen, “Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning,” IEEE Trans. Instrumentation Meas., vol. 67, no. 1, pp. 185195, Jan. 2018. https://doi.org/10.1109/TIM.2017.2759418.

    • Search Google Scholar
    • Export Citation
  • [13]

    Y. Lei, F. Jia, J. Lin, S. Xing, and S. X. Ding, “An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data,” IEEE Trans. Ind. Electron., vol. 63, no. 5, pp. 31373147, May 2016. https://doi.org/10.1109/TIE.2016.2519325.

    • Search Google Scholar
    • Export Citation
  • [14]

    T. Ince, S. Kiranyaz, L. Eren, M. Askar, and M. Gabbouj, “Real-time motor fault detection by 1-D convolutional neural networks,” IEEE Trans. Ind. Electron., vol. 63, no. 11, pp. 70677075, Nov. 2016. https://doi.org/10.1109/TIE.2016.2582729.

    • Search Google Scholar
    • Export Citation
  • [15]

    Z. Ullah, S.-T. Lee, and J. Hur, “A torque angle-based fault detection and identification technique for IPMSM,” IEEE Trans. Industry Appl., vol. 56, no. 1, pp. 170182, Jan.-Feb. 2020. https://doi.org/10.1109/TIA.2019.2947401.

    • Search Google Scholar
    • Export Citation
  • [16]

    C. Lu, Y. Wang, M. Ragulskis, and Y. Cheng, “Fault diagnosis for rotating machinery: a method based on image processing,” PLoS One, Oct. 2016. https://doi.org/10.1371/journal.pone.0164111.

    • Search Google Scholar
    • Export Citation
  • [17]

    E. Balaban, P. Bansal, P. Stoelting, A. Saxena, K. F. Goebel, and S. Curran, “A diagnostic approach for electro-mechanical actuators in aerospace systems,” 2009 IEEE Aerospace Conference, Big Sky, MT, USA, 2009, vol. 4, pp. 113, Feb. 2021. https://doi.org/10.1109/AERO.2009.4839661.

    • Search Google Scholar
    • Export Citation
  • [18]

    M. Hussain, A. R. Rizwan, I. H. Kalwar, and D. T. Memon, “Multiple faults detection and identification of three phase induction motor using advanced signal processing techniques,” 3C Tecnología, Spec. Issue, pp. 93117, Nov. 2020. https://doi.org/10.17993/3ctecno.2020.specialissue6.93-117.

    • Search Google Scholar
    • Export Citation
  • [19]

    R. H. Hadi, H. N. Hady, A. M. Hasan, A. Al-Jodah, and A. J. Humaidi, “Improved Fault classification for predictive maintenance in industrial IoT based on AutoML: a case study of ball-bearing faults,” Processes, vol. 11, no. 5, p. 1507, 2023. https://doi.org/10.3390/pr11051507.

    • Search Google Scholar
    • Export Citation
  • [20]

    A. R. Ajel, A. Q. Al-Dujaili, Z. G. Hadi, and A. J. Humaidi, “Skin cancer classifier based on convolution residual neural network,” Int. J. Electr. Comput. Eng. (IJECE), vol. 13, no. 6, pp. 62406248, 2023. https://doi.org/10.11591/ijece.v13i6.pp6240-6248.

    • Search Google Scholar
    • Export Citation
  • [21]

    A. R. Nasser, A. M. Hasan, and A. J. Humaidi, “DL-AMDet: Deep learning-based malware detector for android,” Intell. Syst. Appl., vol. 21, no. 200318, pp. 110, 2024. https://doi.org/10.1016/j.iswa.2023.200318.

    • Search Google Scholar
    • Export Citation
  • [22]

    J. Abdulhadi, A. Al-Dujaili, A. J. Humaidi, and M. A. R. Fadhel, “Human nail diseases classification based on transfer learning,” ICIC Express Lett., vol. 15, no. 12, pp. 12711282, 2021.

    • Search Google Scholar
    • Export Citation
  • [23]

    H. Al-Khazraji, A. R Nasser, A. M. Hasan, A. K. Al Mhdawi, H. Al-Raweshidy, and A. J. Humaidi, “Aircraft engines remaining useful life prediction based on A hybrid model of autoencoder and deep belief network,” IEEE Access, vol. 10, pp. 8215682163, 2022. https://doi.org/10.1109/ACCESS.2022.3188681.

    • Search Google Scholar
    • Export Citation
  • [24]

    M. Al-Amidie, A. Al-Asadi, A. J. Humaidi, A. Al-Dujaili, L. Alzubaidi, L. Farhan, M. A. Fadhel, R. G. McGarvey, and N. E. Islam, “Robust spectrum sensing detector based on MIMO cognitive radios with non-perfect channel gain,” Electronics, vol. 10, p. 529, 2021. https://doi.org/10.3390/electronics10050529.

    • Search Google Scholar
    • Export Citation
  • [25]

    A. Al-Dujaili, V. Cocquempot, M. E. B. E. Najjar, D. Pereira, and A. Humaidi, “Adaptive fault-tolerant control design for multi-linked two-wheel drive mobile robots. Mobile robot: Motion control and path planning,” 1090, Springer International Publishing, pp. 283329, 2023. https://doi.org/10.1007/978-3-031-26564-8_10, Studies in Computational Intelligence.

    • Search Google Scholar
    • Export Citation
  • [26]

    A. Q. Al-Dujaili, A. J. Humaidi, Z. T. Allawi, and M. E. Sadiq, “Earthquake hazard mitigation for uncertain building systems based on adaptive synergetic control,” Appl. Syst. Innovation, vol. 6, no. 2, p. 34, 2023. https://doi.org/10.3390/asi6020034.

    • Search Google Scholar
    • Export Citation
  • [27]

    A. F. Hasan, N. Al-Shamaa, S. S. Husain, A. J. Humaidi, and A. Al-dujaili, “Spotted hyena optimizer enhances the performance of fractional-order PD controller for tri-copter drone,” Int. Rev. Appl. Sci. Eng., vol. 15, no. 1, pp. 8294, 2023. https://doi.org/10.1556/1848.2023.00659.

    • Search Google Scholar
    • Export Citation
  • [28]

    A. Al-Dujaili, et al., “Fault diagnosis and Fault tolerant control for -linked two wheel drive mobile robots,” in Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol. 1090, A. T. Azar, I. Kasim Ibraheem, and A. Jaleel Humaidi, Eds., Cham: Springer, 2023.

    • Search Google Scholar
    • Export Citation
  • [29]

    A. K. Mohammed, N. K. Al-Shamaa, and A. Q. Al-Dujaili, “Super-Twisting sliding mode control of permanent magnet DC motor,” in 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), May 2022, pp. 347352.

    • Search Google Scholar
    • Export Citation
  • [30]

    A. K. Mohammed, A. Q. Al-Dujaili, and N. K. Al-Shamaa, “Real-time design and implementation of nonlinear speed controller for permanent magnet DC motor based on PSO tuner,” in 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS), 2021, pp. 323328.

    • Search Google Scholar
    • Export Citation
  • [31]

    A. J. Humaidi and M. R. Hameed, “Development of a new adaptive backstepping control design for a non-strict and under-actuated system based on a PSO tuner,” Information (Switzerland), vol. 10, no. 2, supp. 38, pp. 11931204, 2019. https://doi.org/10.3390/info10020038.

    • Search Google Scholar
    • Export Citation
  • [32]

    A. J. Humaidi, M. R. Hameed, A. F. Hasan, A. S. M. Al-Obaidi, A. T. Azar, I. K. Ibraheem, A. Q. Al-Dujaili, A. K. Al Mhdawi, and F. A. Abdulmajeed, “Algorithmic design of block backstepping motion and stabilization control for segway mobile robot,” in Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol. 1090, A. T. Azar, I. Kasim Ibraheem, and A. Jaleel Humaidi, Eds., Cham: Springer, 2023.

    • Search Google Scholar
    • Export Citation
  • [33]

    A. F. Hasan, A. J. Humaidi, A. S. M. Al-Obaidi, A. T. Azar, I. K. Ibraheem, A. Q. Al-Dujaili, A. K. Al-Mhdawi, and F. A. Abdulmajeed, “Fractional order extended state observer enhances the performance of controlled tri-copter UAV based on active disturbance rejection control,” in Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol. 1090, A. T. Azar, I. Kasim Ibraheem, and A. Jaleel Humaidi, Eds., Cham: Springer, 2023.

    • Search Google Scholar
    • Export Citation
  • [34]

    S. S. Husain, A. Q. Al-Dujaili, A. A. Jaber, A. J. Humaidi, and R. S. Al-Azzawi, “Design of a robust controller based on barrier function for vehicle steer-by-wire systems,” World Electr. Veh. J., vol. 15, no. 1, supp. 17, 2024. https://doi.org/10.3390/wevj15010017.

    • Search Google Scholar
    • Export Citation
  • [35]

    A. Hussein, A. T. Abdulameer, A. Abdulkarim, H. Husni, and D. Al-Ubaidi, “Classification of dyslexia among school students using deep learning,” J. Tech., vol. 6, no. 1, pp. 8592, 2024. https://doi.org/10.51173/jt.v6i1.1893.

    • Search Google Scholar
    • Export Citation
  • [36]

    M. Y. Hassan, A. J. Humaidi, and M. K. Hamza, “On the design of backstepping controller for Acrobot system based on adaptive observer,” Int. Rev. Electr. Eng., vol. 15, no. 4, pp. 328335, 2020. https://doi.org/10.15866/iree.v15i4.17827.

    • Search Google Scholar
    • Export Citation
  • [37]

    A. T. Mohammad and J. Parchami, “Improving diabetic patients monitoring system using (NCA-CNN) algorithm based on loT,” J. Tech., vol. 6, no. 2, pp. 917, 2024. https://doi.org/10.51173/jt.v6i2.2316.

    • Search Google Scholar
    • Export Citation
  • [38]

    A. J. Humaidi and M. R. Hameed, “Design and performance investigation of block-backstepping algorithms for ball and arc system,” in IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI 2017, 2018, pp. 325332. https://doi.org/10.1109/ICPCSI.2017.8392309.

    • Search Google Scholar
    • Export Citation
  • [39]

    S. A. AL-Hashemi, A. AL-Dujaili, and A. R. Ajel, “Speed control using an integral sliding mode controller for a three-phase induction motor,” J. Tech., vol. 3, no. 3, pp. 1019, 2021. https://doi.org/10.51173/jt.v3i3.328.

    • Search Google Scholar
    • Export Citation
  • [1]

    A. J. Humaidi and A. H. Hameed, “PMLSM position control based on continuous projection adaptive sliding mode controller,” Syst. Sci. Control Eng., vol. 6, no. 3, 2018. https://doi.org/10.1080/21642583.2018.1547887.

    • Search Google Scholar
    • Export Citation
  • [2]

    B. Aubert, J. Régnier, S. Caux, and D. Alejo, “Kalman-filter-based indicator for online interturn short circuits detection in permanent-magnet synchronous generators,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 19211930, March 2015. https://doi.org/10.1109/TIE.2014.2348934.

    • Search Google Scholar
    • Export Citation
  • [3]

    Z. Gao, C. Cecati, and S. X. Ding, “A survey of fault diagnosis and fault-tolerant techniques—Part I: fault diagnosis with model-based and signal-based approaches,” IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 37573767, June 2015. https://doi.org/10.1109/TIE.2015.2417501.

    • Search Google Scholar
    • Export Citation
  • [4]

    X. Dai and Z. Gao, “From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis,” IEEE Trans. Ind. Inform., vol. 9, no. 4, pp. 22262238, Nov. 2013. https://doi.org/10.1109/TII.2013.2243743.

    • Search Google Scholar
    • Export Citation
  • [5]

    C. G. Dias and F. H. Pereira, “Broken rotor bars detection in induction motors running at very low slip using a hall effect sensor,” IEEE Sensors J., vol. 18, no. 11, pp. 46024613, 1 June, 2018. https://doi.org/10.1109/JSEN.2018.2827204.

    • Search Google Scholar
    • Export Citation
  • [6]

    H. Liu, J. Zhou, Y. Xu, Y. Zheng, X. Peng, and W. Jiang, “Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks,” Neurocomputing, vol. 315, pp. 412424, Nov. 2018. https://doi.org/10.1016/j.neucom.2018.07.034.

    • Search Google Scholar
    • Export Citation
  • [7]

    M. Altobi, G. Bevan, P. Wallace, D. Harrison, and K. Ramachandran, “Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT,” Eng. Sci. Technol. Int. J., vol. 22, no. 3, pp. 854861, Jun. 2019. https://doi.org/10.1016/j.jestch.2019.01.005.

    • Search Google Scholar
    • Export Citation
  • [8]

    S. Moosavi, A. Djerdir, Y. Amirat, and D. Khaburi, “ANN-based fault diagnosis of permanent magnet synchronous motor under stator winding shorted turn,” Electric Power Syst. Res., vol. 125, pp. 6782, Aug. 2015. https://doi.org/10.1016/j.epsr.2015.03.024.

    • Search Google Scholar
    • Export Citation
  • [9]

    W. Ahmad, S. A. Khan, and J.-M. Kim, “A hybrid prognostics technique for rolling element bearings using adaptive predictive models,” IEEE Trans. Ind. Electron., vol. 65, no. 2, pp. 15771584, Feb. 2018. https://doi.org/10.1109/TIE.2017.2733487.

    • Search Google Scholar
    • Export Citation
  • [10]

    W. Abed, Q. Aish, and A. Hamed, “Performance monitoring of aircraft PMSM based on soft computing technique,” Periodicals Eng. Nat. Sci. (Pen), vol. 9, no. 1, p. 293, 2021. https://doi.org/10.21533/pen.v9i1.1797.

    • Search Google Scholar
    • Export Citation
  • [11]

    Y. Chen, S. Liang, W. Li, H. Liang, and C. Wang, “Faults and diagnosis methods of permanent magnet synchronous motors,” Appl. Sci., vol. 9, no. 10, p. 2116, May. 2019. https://doi.org/10.3390/app9102116.

    • Search Google Scholar
    • Export Citation
  • [12]

    J. Sun, C. Yan, and J. Wen, “Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning,” IEEE Trans. Instrumentation Meas., vol. 67, no. 1, pp. 185195, Jan. 2018. https://doi.org/10.1109/TIM.2017.2759418.

    • Search Google Scholar
    • Export Citation
  • [13]

    Y. Lei, F. Jia, J. Lin, S. Xing, and S. X. Ding, “An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data,” IEEE Trans. Ind. Electron., vol. 63, no. 5, pp. 31373147, May 2016. https://doi.org/10.1109/TIE.2016.2519325.

    • Search Google Scholar
    • Export Citation
  • [14]

    T. Ince, S. Kiranyaz, L. Eren, M. Askar, and M. Gabbouj, “Real-time motor fault detection by 1-D convolutional neural networks,” IEEE Trans. Ind. Electron., vol. 63, no. 11, pp. 70677075, Nov. 2016. https://doi.org/10.1109/TIE.2016.2582729.

    • Search Google Scholar
    • Export Citation
  • [15]

    Z. Ullah, S.-T. Lee, and J. Hur, “A torque angle-based fault detection and identification technique for IPMSM,” IEEE Trans. Industry Appl., vol. 56, no. 1, pp. 170182, Jan.-Feb. 2020. https://doi.org/10.1109/TIA.2019.2947401.

    • Search Google Scholar
    • Export Citation
  • [16]

    C. Lu, Y. Wang, M. Ragulskis, and Y. Cheng, “Fault diagnosis for rotating machinery: a method based on image processing,” PLoS One, Oct. 2016. https://doi.org/10.1371/journal.pone.0164111.

    • Search Google Scholar
    • Export Citation
  • [17]

    E. Balaban, P. Bansal, P. Stoelting, A. Saxena, K. F. Goebel, and S. Curran, “A diagnostic approach for electro-mechanical actuators in aerospace systems,” 2009 IEEE Aerospace Conference, Big Sky, MT, USA, 2009, vol. 4, pp. 113, Feb. 2021. https://doi.org/10.1109/AERO.2009.4839661.

    • Search Google Scholar
    • Export Citation
  • [18]

    M. Hussain, A. R. Rizwan, I. H. Kalwar, and D. T. Memon, “Multiple faults detection and identification of three phase induction motor using advanced signal processing techniques,” 3C Tecnología, Spec. Issue, pp. 93117, Nov. 2020. https://doi.org/10.17993/3ctecno.2020.specialissue6.93-117.

    • Search Google Scholar
    • Export Citation
  • [19]

    R. H. Hadi, H. N. Hady, A. M. Hasan, A. Al-Jodah, and A. J. Humaidi, “Improved Fault classification for predictive maintenance in industrial IoT based on AutoML: a case study of ball-bearing faults,” Processes, vol. 11, no. 5, p. 1507, 2023. https://doi.org/10.3390/pr11051507.

    • Search Google Scholar
    • Export Citation
  • [20]

    A. R. Ajel, A. Q. Al-Dujaili, Z. G. Hadi, and A. J. Humaidi, “Skin cancer classifier based on convolution residual neural network,” Int. J. Electr. Comput. Eng. (IJECE), vol. 13, no. 6, pp. 62406248, 2023. https://doi.org/10.11591/ijece.v13i6.pp6240-6248.

    • Search Google Scholar
    • Export Citation
  • [21]

    A. R. Nasser, A. M. Hasan, and A. J. Humaidi, “DL-AMDet: Deep learning-based malware detector for android,” Intell. Syst. Appl., vol. 21, no. 200318, pp. 110, 2024. https://doi.org/10.1016/j.iswa.2023.200318.

    • Search Google Scholar
    • Export Citation
  • [22]

    J. Abdulhadi, A. Al-Dujaili, A. J. Humaidi, and M. A. R. Fadhel, “Human nail diseases classification based on transfer learning,” ICIC Express Lett., vol. 15, no. 12, pp. 12711282, 2021.

    • Search Google Scholar
    • Export Citation
  • [23]

    H. Al-Khazraji, A. R Nasser, A. M. Hasan, A. K. Al Mhdawi, H. Al-Raweshidy, and A. J. Humaidi, “Aircraft engines remaining useful life prediction based on A hybrid model of autoencoder and deep belief network,” IEEE Access, vol. 10, pp. 8215682163, 2022. https://doi.org/10.1109/ACCESS.2022.3188681.

    • Search Google Scholar
    • Export Citation
  • [24]

    M. Al-Amidie, A. Al-Asadi, A. J. Humaidi, A. Al-Dujaili, L. Alzubaidi, L. Farhan, M. A. Fadhel, R. G. McGarvey, and N. E. Islam, “Robust spectrum sensing detector based on MIMO cognitive radios with non-perfect channel gain,” Electronics, vol. 10, p. 529, 2021. https://doi.org/10.3390/electronics10050529.

    • Search Google Scholar
    • Export Citation
  • [25]

    A. Al-Dujaili, V. Cocquempot, M. E. B. E. Najjar, D. Pereira, and A. Humaidi, “Adaptive fault-tolerant control design for multi-linked two-wheel drive mobile robots. Mobile robot: Motion control and path planning,” 1090, Springer International Publishing, pp. 283329, 2023. https://doi.org/10.1007/978-3-031-26564-8_10, Studies in Computational Intelligence.

    • Search Google Scholar
    • Export Citation
  • [26]

    A. Q. Al-Dujaili, A. J. Humaidi, Z. T. Allawi, and M. E. Sadiq, “Earthquake hazard mitigation for uncertain building systems based on adaptive synergetic control,” Appl. Syst. Innovation, vol. 6, no. 2, p. 34, 2023. https://doi.org/10.3390/asi6020034.

    • Search Google Scholar
    • Export Citation
  • [27]

    A. F. Hasan, N. Al-Shamaa, S. S. Husain, A. J. Humaidi, and A. Al-dujaili, “Spotted hyena optimizer enhances the performance of fractional-order PD controller for tri-copter drone,” Int. Rev. Appl. Sci. Eng., vol. 15, no. 1, pp. 8294, 2023. https://doi.org/10.1556/1848.2023.00659.

    • Search Google Scholar
    • Export Citation
  • [28]

    A. Al-Dujaili, et al., “Fault diagnosis and Fault tolerant control for -linked two wheel drive mobile robots,” in Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol. 1090, A. T. Azar, I. Kasim Ibraheem, and A. Jaleel Humaidi, Eds., Cham: Springer, 2023.

    • Search Google Scholar
    • Export Citation
  • [29]

    A. K. Mohammed, N. K. Al-Shamaa, and A. Q. Al-Dujaili, “Super-Twisting sliding mode control of permanent magnet DC motor,” in 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), May 2022, pp. 347352.

    • Search Google Scholar
    • Export Citation
  • [30]

    A. K. Mohammed, A. Q. Al-Dujaili, and N. K. Al-Shamaa, “Real-time design and implementation of nonlinear speed controller for permanent magnet DC motor based on PSO tuner,” in 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS), 2021, pp. 323328.

    • Search Google Scholar
    • Export Citation
  • [31]

    A. J. Humaidi and M. R. Hameed, “Development of a new adaptive backstepping control design for a non-strict and under-actuated system based on a PSO tuner,” Information (Switzerland), vol. 10, no. 2, supp. 38, pp. 11931204, 2019. https://doi.org/10.3390/info10020038.

    • Search Google Scholar
    • Export Citation
  • [32]

    A. J. Humaidi, M. R. Hameed, A. F. Hasan, A. S. M. Al-Obaidi, A. T. Azar, I. K. Ibraheem, A. Q. Al-Dujaili, A. K. Al Mhdawi, and F. A. Abdulmajeed, “Algorithmic design of block backstepping motion and stabilization control for segway mobile robot,” in Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol. 1090, A. T. Azar, I. Kasim Ibraheem, and A. Jaleel Humaidi, Eds., Cham: Springer, 2023.

    • Search Google Scholar
    • Export Citation
  • [33]

    A. F. Hasan, A. J. Humaidi, A. S. M. Al-Obaidi, A. T. Azar, I. K. Ibraheem, A. Q. Al-Dujaili, A. K. Al-Mhdawi, and F. A. Abdulmajeed, “Fractional order extended state observer enhances the performance of controlled tri-copter UAV based on active disturbance rejection control,” in Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol. 1090, A. T. Azar, I. Kasim Ibraheem, and A. Jaleel Humaidi, Eds., Cham: Springer, 2023.

    • Search Google Scholar
    • Export Citation
  • [34]

    S. S. Husain, A. Q. Al-Dujaili, A. A. Jaber, A. J. Humaidi, and R. S. Al-Azzawi, “Design of a robust controller based on barrier function for vehicle steer-by-wire systems,” World Electr. Veh. J., vol. 15, no. 1, supp. 17, 2024. https://doi.org/10.3390/wevj15010017.

    • Search Google Scholar
    • Export Citation
  • [35]

    A. Hussein, A. T. Abdulameer, A. Abdulkarim, H. Husni, and D. Al-Ubaidi, “Classification of dyslexia among school students using deep learning,” J. Tech., vol. 6, no. 1, pp. 8592, 2024. https://doi.org/10.51173/jt.v6i1.1893.

    • Search Google Scholar
    • Export Citation
  • [36]

    M. Y. Hassan, A. J. Humaidi, and M. K. Hamza, “On the design of backstepping controller for Acrobot system based on adaptive observer,” Int. Rev. Electr. Eng., vol. 15, no. 4, pp. 328335, 2020. https://doi.org/10.15866/iree.v15i4.17827.

    • Search Google Scholar
    • Export Citation
  • [37]

    A. T. Mohammad and J. Parchami, “Improving diabetic patients monitoring system using (NCA-CNN) algorithm based on loT,” J. Tech., vol. 6, no. 2, pp. 917, 2024. https://doi.org/10.51173/jt.v6i2.2316.

    • Search Google Scholar
    • Export Citation
  • [38]

    A. J. Humaidi and M. R. Hameed, “Design and performance investigation of block-backstepping algorithms for ball and arc system,” in IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI 2017, 2018, pp. 325332. https://doi.org/10.1109/ICPCSI.2017.8392309.

    • Search Google Scholar
    • Export Citation
  • [39]

    S. A. AL-Hashemi, A. AL-Dujaili, and A. R. Ajel, “Speed control using an integral sliding mode controller for a three-phase induction motor,” J. Tech., vol. 3, no. 3, pp. 1019, 2021. https://doi.org/10.51173/jt.v3i3.328.

    • Search Google Scholar
    • Export Citation
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Senior editors

Editor-in-Chief: Ákos, Lakatos University of Debrecen (Hungary)

Founder, former Editor-in-Chief (2011-2020): Ferenc Kalmár University of Debrecen (Hungary)

Founding Editor: György Csomós University of Debrecen (Hungary)

Associate Editor: Derek Clements Croome University of Reading (UK)

Associate Editor: Dezső Beke University of Debrecen (Hungary)

Editorial Board

  • Mohammad Nazir AHMAD Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

    Murat BAKIROV Center for Materials and Lifetime Management Ltd., Moscow, Russia

    Nicolae BALC Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Umberto BERARDI Ryerson University Toronto, Toronto, Canada

    Ildikó BODNÁR University of Debrecen, Debrecen, Hungary

    Sándor BODZÁS University of Debrecen, Debrecen, Hungary

    Fatih Mehmet BOTSALI Selçuk University, Konya, Turkey

    Samuel BRUNNER Empa Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

    István BUDAI University of Debrecen, Debrecen, Hungary

    Constantin BUNGAU University of Oradea, Oradea, Romania

    Shanshan CAI Huazhong University of Science and Technology, Wuhan, China

    Michele De CARLI University of Padua, Padua, Italy

    Robert CERNY Czech Technical University in Prague, Prague, Czech Republic

    Erdem CUCE Recep Tayyip Erdogan University, Rize, Turkey

    György CSOMÓS University of Debrecen, Debrecen, Hungary

    Tamás CSOKNYAI Budapest University of Technology and Economics, Budapest, Hungary

    Anna FORMICA IASI National Research Council, Rome, Italy

    Alexandru GACSADI University of Oradea, Oradea, Romania

    Eugen Ioan GERGELY University of Oradea, Oradea, Romania

    Janez GRUM University of Ljubljana, Ljubljana, Slovenia

    Géza HUSI University of Debrecen, Debrecen, Hungary

    Ghaleb A. HUSSEINI American University of Sharjah, Sharjah, United Arab Emirates

    Nikolay IVANOV Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Antal JÁRAI Eötvös Loránd University, Budapest, Hungary

    Gudni JÓHANNESSON The National Energy Authority of Iceland, Reykjavik, Iceland

    László KAJTÁR Budapest University of Technology and Economics, Budapest, Hungary

    Ferenc KALMÁR University of Debrecen, Debrecen, Hungary

    Tünde KALMÁR University of Debrecen, Debrecen, Hungary

    Milos KALOUSEK Brno University of Technology, Brno, Czech Republik

    Jan KOCI Czech Technical University in Prague, Prague, Czech Republic

    Vaclav KOCI Czech Technical University in Prague, Prague, Czech Republic

    Imra KOCSIS University of Debrecen, Debrecen, Hungary

    Imre KOVÁCS University of Debrecen, Debrecen, Hungary

    Angela Daniela LA ROSA Norwegian University of Science and Technology, Trondheim, Norway

    Éva LOVRA Univeqrsity of Debrecen, Debrecen, Hungary

    Elena LUCCHI Eurac Research, Institute for Renewable Energy, Bolzano, Italy

    Tamás MANKOVITS University of Debrecen, Debrecen, Hungary

    Igor MEDVED Slovak Technical University in Bratislava, Bratislava, Slovakia

    Ligia MOGA Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Marco MOLINARI Royal Institute of Technology, Stockholm, Sweden

    Henrieta MORAVCIKOVA Slovak Academy of Sciences, Bratislava, Slovakia

    Phalguni MUKHOPHADYAYA University of Victoria, Victoria, Canada

    Balázs NAGY Budapest University of Technology and Economics, Budapest, Hungary

    Husam S. NAJM Rutgers University, New Brunswick, USA

    Jozsef NYERS Subotica Tech College of Applied Sciences, Subotica, Serbia

    Bjarne W. OLESEN Technical University of Denmark, Lyngby, Denmark

    Stefan ONIGA North University of Baia Mare, Baia Mare, Romania

    Joaquim Norberto PIRES Universidade de Coimbra, Coimbra, Portugal

    László POKORÁDI Óbuda University, Budapest, Hungary

    Roman RABENSEIFER Slovak University of Technology in Bratislava, Bratislava, Slovak Republik

    Mohammad H. A. SALAH Hashemite University, Zarqua, Jordan

    Dietrich SCHMIDT Fraunhofer Institute for Wind Energy and Energy System Technology IWES, Kassel, Germany

    Lorand SZABÓ Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Csaba SZÁSZ Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Ioan SZÁVA Transylvania University of Brasov, Brasov, Romania

    Péter SZEMES University of Debrecen, Debrecen, Hungary

    Edit SZŰCS University of Debrecen, Debrecen, Hungary

    Radu TARCA University of Oradea, Oradea, Romania

    Zsolt TIBA University of Debrecen, Debrecen, Hungary

    László TÓTH University of Debrecen, Debrecen, Hungary

    László TÖRÖK University of Debrecen, Debrecen, Hungary

    Anton TRNIK Constantine the Philosopher University in Nitra, Nitra, Slovakia

    Ibrahim UZMAY Erciyes University, Kayseri, Turkey

    Tibor VESSELÉNYI University of Oradea, Oradea, Romania

    Nalinaksh S. VYAS Indian Institute of Technology, Kanpur, India

    Deborah WHITE The University of Adelaide, Adelaide, Australia

International Review of Applied Sciences and Engineering
Address of the institute: Faculty of Engineering, University of Debrecen
H-4028 Debrecen, Ótemető u. 2-4. Hungary
Email: irase@eng.unideb.hu

Indexing and Abstracting Services:

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  • SCOPUS
  • Ulrich's Periodicals Directory

 

2022  
Scimago  
Scimago
H-index
9
Scimago
Journal Rank
0.235
Scimago Quartile Score Architecture (Q2)
Engineering (miscellaneous) (Q3)
Environmental Engineering (Q3)
Information Systems (Q4)
Management Science and Operations Research (Q4)
Materials Science (miscellaneous) Q3)
Scopus  
Scopus
Cite Score
1.6
Scopus
CIte Score Rank
Architecture 46/170 (73rd PCTL)
General Engineering 174/302 (42nd PCTL)
Materials Science (miscellaneous) 93/150 (38th PCTL)
Environmental Engineering 123/184 (33rd PCTL)
Management Science and Operations Research 142/198 (28th PCTL)
Information Systems 281/379 (25th PCTL)
 
Scopus
SNIP
0.686

2021  
Scimago  
Scimago
H-index
7
Scimago
Journal Rank
0,199
Scimago Quartile Score Engineering (miscellaneous) (Q3)
Environmental Engineering (Q4)
Information Systems (Q4)
Management Science and Operations Research (Q4)
Materials Science (miscellaneous) (Q4)
Scopus  
Scopus
Cite Score
1,2
Scopus
CIte Score Rank
Architecture 48/149 (Q2)
General Engineering 186/300 (Q3)
Materials Science (miscellaneous) 79/124 (Q3)
Environmental Engineering 118/173 (Q3)
Management Science and Operations Research 141/184 (Q4)
Information Systems 274/353 (Q4)
Scopus
SNIP
0,457

2020  
Scimago
H-index
5
Scimago
Journal Rank
0,165
Scimago
Quartile Score
Engineering (miscellaneous) Q3
Environmental Engineering Q4
Information Systems Q4
Management Science and Operations Research Q4
Materials Science (miscellaneous) Q4
Scopus
Cite Score
102/116=0,9
Scopus
Cite Score Rank
General Engineering 205/297 (Q3)
Environmental Engineering 107/146 (Q3)
Information Systems 269/329 (Q4)
Management Science and Operations Research 139/166 (Q4)
Materials Science (miscellaneous) 64/98 (Q3)
Scopus
SNIP
0,26
Scopus
Cites
57
Scopus
Documents
36
Days from submission to acceptance 84
Days from acceptance to publication 348
Acceptance
Rate

23%

 

2019  
Scimago
H-index
4
Scimago
Journal Rank
0,229
Scimago
Quartile Score
Engineering (miscellaneous) Q2
Environmental Engineering Q3
Information Systems Q3
Management Science and Operations Research Q4
Materials Science (miscellaneous) Q3
Scopus
Cite Score
46/81=0,6
Scopus
Cite Score Rank
General Engineering 227/299 (Q4)
Environmental Engineering 107/132 (Q4)
Information Systems 259/300 (Q4)
Management Science and Operations Research 136/161 (Q4)
Materials Science (miscellaneous) 60/86 (Q3)
Scopus
SNIP
0,866
Scopus
Cites
35
Scopus
Documents
47
Acceptance
Rate
21%

 

International Review of Applied Sciences and Engineering
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 1100 EUR/article
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Limited number of full waiver available. Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access

International Review of Applied Sciences and Engineering
Language English
Size A4
Year of
Foundation
2010
Volumes
per Year
1
Issues
per Year
3
Founder Debreceni Egyetem
Founder's
Address
H-4032 Debrecen, Hungary Egyetem tér 1
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-0810 (Print)
ISSN 2063-4269 (Online)

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May 2024 0 65 26
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