SAIDA Dahmane
دحمان سعيدة
saida.dahmene@univ-msila.dz
0662108629
- DEPARTEMENT OF: ELECTRICAL ENGINEERING
- Faculty of Technology
- Grade PHd
About Me
Science et Technologies
Filiere
Electromécanique
Location
Msila, Msila
Msila, ALGERIA
- 1995-01-09 00:00:00
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SAIDA Dahmane birthday
- 2024-03-20
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2024-03-20
Diagnosis and Monitoring Method for Detecting and Localizing Bearing Faults
Induction motors in modern industry are becoming more and more functional and complex. Unfortunately, these machines are not free from damages what make their fault diagnosis the most critical aspect of system monitoring and maintenance. Vibrational signal data yields relevant information about the state of the entire system, as well as specifically about one of its components that makes its analysis quite interesting. For this effect, the current paper aims to propose an automatic diagnosis and monitoring method for detecting and locating bearing faults in an induction motor based on vibration signal processing. The suggested method combines the discrete wavelet transform (DWT) with the envelope spectrum (ENV) as advanced signal processing, incorporating a machine learning algorithm based on random forest classifier. The discrete wavelet transforms (DWT), using the Haar wavelet, decomposes the vibrational signal to provide both approximations and details. Each detail is then reconstructed to avoid any missing of information. To precisely select the reconstructed detail 〖(Recd〗_k) that provides pertinent information about bearing faults, a statistical study is conducted. This study involves calculating four indicators (Root mean square (RMS), correlation coefficient (CC), energy coefficient (EC) and peak to peak (P2P) factor) is performed for each 〖(Recd〗_k). These indicators are compared with threshold indicators, and this criterion is met by the reconstructed details 1 and 3. The obtained reconstructed details are then subjected to the spectral envelope analysis to detect the fault frequencies, which are considered as new features entering the random forest classifier model. This combination of approaches allows better feature extraction and structuring of the dataset, leading to improved accuracy of the random forest classifier, achieving a higher classification rate of more than 99,53 %. The proposed DWT-ENV-RF method indicates well its efficiency when compared to other recent works, and the attained results are all confirmed by the experimental tests conducted in the CWRU laboratory.
Citation
SAIDA Dahmane , , (2024-03-20), Diagnosis and Monitoring Method for Detecting and Localizing Bearing Faults, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), Vol:12, Issue:1, pages:16, Munawar Riadi
- 2023
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2023
The suitable accelerometer parameter for minimizing uncertainty
Due to their benefits, accelerometers are now widely used in a variety of fields. As a result, various accelerometer types are introduced in this paper, along with their applications in engineering issues. The capacitive accelerometers are then compared to other types, and a mathematical model for them is presented. Furthermore, the effect of damping rate selection on measurement error and accelerometer performance is investigated. Finally, compare the previous damping rate value to the proposed appropriate choice of value to reduce measurement error to less than 0.02 percent 0,11%.
Citation
Mohamed Razi morakchi , SAIDA Dahmane , atef chibani, ,(2023), The suitable accelerometer parameter for minimizing uncertainty,3rd International Conference on Engineering and Applied Natural Sciences,Konya,Turkey
- 2022
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2022
comparative study for capacitance variation for three damping ratios of capacitive accelerometer
The rapid advancement of (microelectromechanical system) MEMS-based accelerometer technology is due to their numerous benefits, including high sensitivity, low power consumption, minimal susceptibility to temperature variation, and low fabrication cost [1][2][3], The accelerometer has a wide spectrum of uses, including:s aeronautics[4], seismometer in mars [5], gravitational field gyroscope [6], gyroscope of satellite [7] and its navigation [8]. Wireless ElectroCardioGram (ECG) and heart monitoring [9]. The method and mode of detection identify the type of accelerometer such as :piezoresistive based on the change of resistance[10],tunneling based on the change of current[8] optical, the working principle is based on the variation of the output light intensity versus the acceleration [7], capacitive based on the change of capacitance between electrodes[11] and piezoelectric a microscopic crystal arrangement gets strained by accelerating forces, and that generates an equal amount of potential difference. [12]. The capacitive is chosen and widely used in various fields due to their advantages approach to other design methodology [11].In our work, a comparative study of capacitance variation for its interesting advantages. In this simulation analysis, a mathematical relationship for parallel and is established between capacitance variation as function of displacement. Fig 1 as bellows shows an illustration of two parallel beams for capacity of accelerometer. Then according According to fig.2, our suggested model has a higher sensitivity than the proposed models in [11] and [12]. The difference is not particularly noticeable, but it has a significant impact on the stability system. Finally, we conclude that the proposed value of damping rate makes it possible for develop a model of accelerometer more accurate and sensitive in terms of capacity variations. in future work we can extend to develop a novel design of capacitive accelerometer.
Citation
Mohamed Razi morakchi , SAIDA Dahmane , selman djeffal, ,(2022), comparative study for capacitance variation for three damping ratios of capacitive accelerometer,The first national conference in Materials Science and Engineering (NCMSE’1_2022),,khemis meliana
- 2022
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2022
A Rolling Bearing Fault Identification Based on Vibration Signals Analysis Using DWT-DFT technique.
Abstract – Nowadays, the induction motors take preponderant place in modern industrial applications. However, due to hard operating conditions, these motors are exposed to different forms of damages that affects the stator, the rotor and the bearings. The bearing faults represent about 41 % of all other fault’s occurrences. In addition, the monitoring of these bearings can explored through the use of several physical quantities among them the vibration analysis is the most exploited to monitor and diagnose bearing defects. In this paper, we propose a diagnosis method for the identification of bearing faults by combining the Discret Wavelet Transform (DWT) and the Discret Fourier Transform (DFT). The DWT decomposes the signals corresponding to the two cases: outer race fault and inner race fault, to obtain details coefficients. A statistical study based on RMS and peak-to-peak factor, is then applied to the obtained details in order to select the optimum wavelet detail containing necessary harmonics conforming to the fault cases. The selection of the details obtained from the wavelet decomposition must respond to the highest value of both the RMS (Root Mean Square) and the peak-to-peak factor. The DFT is thereafter applied to the selected details to obtain the frequency spectrum for extracting the frequencies’ characteristics of bearing faults. The fault frequencies calculated theoretically that following the equations from (1) to (3) mentioned in [1], and exploiting the data of vibration signals measured at a sampling frequency of 12000 Hz and a motor speed of 1772 RPM, available in CWRU [2] are: the rotation frequency 𝑓𝑟 = 29.53 Hz, the outer race fault frequency 𝑓𝑜𝑟 =105.64 Hz and the inner race fault frequency 𝑓𝑖𝑟 =160.16 Hz. The results obtained by the Discret Fourier Transform are compared with those obtained theoretically that demonstrates well the effectiveness of the proposed method.
Citation
SAIDA Dahmane , FOUAD Berrabah , Mohamed Razi morakchi , ,(2022), A Rolling Bearing Fault Identification Based on Vibration Signals Analysis Using DWT-DFT technique.,4th International Conference on Applied Engineering and Natural Sciences,konya
- 2022
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2022
Experimental Study of Bearing Fault Diagnosis Using Vibration Signal - Kurtogram
Abstract. The maintenance of the induction motor by the mean of vibration analysis has advanced significantly in recent years, thanks to sophisticated signal processing techniques. These techniques allow detecting at an early state the existence of a fault. The present paper proposes a diagnosis method for detecting bearing faults. This method uses the Kurtogram for diagnosing the bearing fault based on the frequency characterizing fault of the inner and outer race. The results attained are all verified after many experimental tests conducted in the CWRU.
Citation
SAIDA Dahmane , FOUAD Berrabah , MABROUK Defdaf , ,(2022), Experimental Study of Bearing Fault Diagnosis Using Vibration Signal - Kurtogram,Le 2 iéme Séminaire International en Génie Industriel et Mathématiques Appliquées,skikda
- 2022
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2022
An Automatic Diagnosis of Bearing Faults of an Induction Motor Based on FFT-ANN
The present paper proposes a diagnosis and monitoring method for detecting and locating bearing faults in an induction motor based on vibration signal processing. The proposed method served to combine Fast Fourier Transform as an advanced signal-processing tool with the Artificial Neural Network (ANN). This study starts in a first stage with the application of the FFT in order to extract frequencies characterizing the fault of the three vibration signals , and . These frequencies will be used in a second stage as inputs of the proposed ANN to locate the bearing’s undamaged components. The features extracted in this study for training the ANN model are the , , , and . Therefore, the results generated by ANN indicate a satisfactory outcome with a higher classification rate of 98.93 %. The suggested FFT-ANN method successfully demonstrates its effectiveness, and the acquired results are completely validated by experiments carried out in the CWRU.
Citation
SAIDA Dahmane , FOUAD Berrabah , MABROUK Defdaf , ,(2022), An Automatic Diagnosis of Bearing Faults of an Induction Motor Based on FFT-ANN,icateee2022,msila
- 2022
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2022
Discret Wavelet Transform (DWT) for Detection of a Rolling Element Bearing Based on Kurtosis-Energy Selection.
Recently, fault detection in asynchronous motors has paid attention of many researchers. The monitoring of these machines is performed through of the use of several physical quantities, among them vibration analysis has a crucial importance for early detection of rolling bearing faults in induction motor (IM), which they represent about 41% of IM’s ensemble defects. Commonly, the induction motor operates under non-stationary operating conditions (varying speed, fluctuating load …), and that leads to the birth of non-stationary vibration signals. The vibration signals produced from the bearing cannot generate any information about the state of the machine. Therefore, a proper analysis of these signals by means of different signal processing tools allows us to determine if the entire rotating machinery is in a normal or abnormal state. In the field of bearing fault detection, signal processing though of fault diagnosis methods has taken preponderant place. Among these methods, Fast Fourier Transform is most frequently used enabling the signal decomposition without losing any information, but it is limited to non-stationary signals such as it cannot provide the temporal location of the appearance of another shock after the born of a first one. To overcome this limitation the Discret Wavelet Transform is used providing both of time and frequency location. In this paper, we propose a diagnosis method for the identification of bearing faults, which serves to combine the DWT (Discret Wavelet Transform) and the envelope analysis. The DWT decomposes the signals of the outer race defect and the inner race defect of the bearing to obtain details. These details will be then subjected to a statistical analysis based on the kurtosis and energy coefficient (EC) in order to select the optimum wavelet details including significant harmonics corresponding to the fault cases. The envelope analysis is then applied to the selected details for extracting the frequencies’ characteristics of bearing faults. The calculated theoretical faults following the equations (1)-(3) mentioned in [1] are the rotation frequency 𝑓𝑟=28.83 Hz; the inner race frequency 𝑓𝑖𝑟=156.34 Hz; the outer race frequency 𝑓𝑜𝑟=103.12 Hz, this all from exploiting the data of vibration signals that are measured at a sampling frequency of 12000 Hz and a motor speed of 1730 RPM, available in CWRU [2]. The selection of the details obtained from the signals corresponding to the outer race and the inner race defects must respond to the greatest value of both the kurtosis and the energy value. As shown in table I, detail 1 matches to the greatest value of the kurtosis and the EC. For the healthy bearing case, the chosen detail has the smallest values that confirms its undamaged case. Taken into account that a value of kurtosis lower than 3 belongs to a good state of the bearing. The results obtained by this combined method are compared with those obtained theoretically that demonstrates well its effectiveness.
Citation
SAIDA Dahmane , FOUAD Berrabah , BILAL DJAMAL EDDINE Cherif , Mohamed Razi morakchi , ,(2022), Discret Wavelet Transform (DWT) for Detection of a Rolling Element Bearing Based on Kurtosis-Energy Selection.,The First National Conference on Materials Science and Engineering (NCMSE'1_2022),Algiers Algeria