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Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This p...
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Citations
... Babanezha et al. [23] introduce a technique using a Probabilistic Neural Network (PNN) to estimate turn-to-turn faults in IMs, evaluating the negative sequence current to estimate up to 1 SCT. Maraaba et al. [24] present another method that applies a multilayer feedforward neural network (MFNN) to diagnose the stator winding faults mean by statistical coefficients and frequency features, reaching a detection of 5% of SCTs. Also, Guedidi et al. [25] employ a modified SqueezNet, a convolutional neural network (CNN), to develop the fault detection of ITSC fault in IMs using 3D images generated from image transformation based on Hilbert transform (HT) and variational mode decomposition (VMD), reaching the detection of five SCTs. ...
... According to this table, it is possible to note that the ANNs at different levels of complexity are applied to the incipient ITSC fault diagnosis. While other techniques present effectiveness in lower levels of ITSC damage with diverse levels of mechanical load [10,24,25] or under a single or no mechanical load [7,11,23,26], they apply elaborated preprocessing stages, which increase their computational complexity and could limit their implementation in an industrial process. In this regard, for example, Guedidi et al. [25] investigate a SqueezeNet model, a lightweight CNN network, for detecting ITSCs in an IM, reaching high accuracy rate. ...
Induction motors are one of the most used machines because they provide the necessary traction force for many industrial applications. Their easy operation, installation, maintenance, and reliability make them preferred over other electrical motors. Mechanical and electrical failures, as with other machines, can appear at any stage of their service life, making the stator intern-turn short-circuit fault (ITSC) stand out. Hence, its detection is necessary in order to extend and save useful life, avoiding a breakdown and unprogrammed maintenance processes as well as, in the worst circumstances, a total loss of the machine. Nonetheless, the challenge lies in detecting this type of fault, which has made the analysis and diagnosis processes easier. Such is the case with convolutional neural networks (CNNs), which facilitate the development of methodologies for pattern recognition in several areas of knowledge. Unfortunately, these techniques require a large amount of data for an adequate training process, which is not always available. In this sense, this paper presents a new methodology for the detection of incipient ITSC faults employing a modified cumulative distribution function (CDF) of the current stator signal. Then, these are converted to images and fed into a fast and compact CNN model, trained with a small data set, reaching up to 99.16% accuracy for seven conditions (0, 5, 10, 15, 20, 30, and 40 short-circuited turns) and four mechanical load conditions.
... The turn failure was detected with 95% accuracy and the level of the turn fault was classified with 90.3% accuracy [18]. Maraaba et al. [31] used statistical features such asmaximum value, variance, minimum value and 2nd harmonic amplitude-obtained from the electromechanical torque as input data to the ANN for detection of ISCF in IMs with different turn faults. The detection and the classification of ISCF were estimated with 88%-99% accuracy. ...
... However, the rate of detected turn fault was 25%. Maraaba et al. [31] have suggested an ANN-based approach for the ISCF detection in the IM. In feature selection, variance, maximum, average and minimum features of the torque in the time domain and the torque's second harmonic in the frequency domain were used. ...
... With the proposed approach, the diagnosis of the turn fault was estimated with 88%-99% accuracy. However, the proposed approach did not present experimental results, only simulation model results [31]. Haddad et al. [9] used the variation of the voltage components on the d and q axes as the fault indicator for ISCF diagnosis. ...
This article presents a novel approach based on the electromechanical torque signal for the inter-turn short-circuit fault (ISCF) detection and the ISCF severity estimation in permanent magnet synchronous motors (PMSMs). The electromechanical torque data have been obtained experimentally in the healthy condition and in three various states of the ISCF at various load rates and at various operating speeds. To extract the features to be used in the ISCF diagnosis, the fast Fourier transform (FFT) implemented to the torque signal. The torque’s second and fourth harmonics were found to be new turn fault features that could be used for ISCF diagnosis. These features were used to train and test the classification algorithms. Four classification algorithms were used to detect ISCF and determine the severity of ISCF: decision trees (DT), artificial neural networks (ANN), K-nearest neighbor (KNN) and support vector machines (SVM). Classification accuracies of 100%, 99.30%, 97.91% and 95.48% were achieved by the ANN, SVM, KNN and DT classifiers, respectively. High accuracy ISCF detection and high accuracy ISCF severity estimation were performed using the developed diagnostic method based on the torque signal.
... Utilizing the motor flux is also another approach [57][58][59][60][61][62]. Some studies used motor torque [63] and acoustic signals [64,65] as fault identifiers. The advantage of using flux signals is to detect failures in their incipient stages. ...
... The detection accuracies are up to 100% for different fault cases. Utilization of torque signal to detect stator faults while deciding severity is shown in [63]. Startup torques with different severity levels are fed into an NN with one hidden layer. ...
Condition monitoring in electric motor drives is essential for operation continuity. This article provides a review of fault detection and diagnosis (FDD) methods for electric motor drives. It first covers various types of faults, their mechanisms, and approaches to detect and diagnose them. The article categorizes faults into machine faults, power electronics (PE) faults, DC link capacitor faults, and sensor faults, and discusses FDD methods. FDD methods for machines are categorized as statistical methods, machine-learning methods, and deep-learning methods. PE FDD methods are divided into logic-based, residual-based, and controller-aided methods. DC link capacitor and sensor faults are briefly explained. Machine and PE faults are listed and presented as tables for easy comparison and fast referencing. Most papers are selected from the past five years but older references are added when necessary. Finally, a discussion section is added to reflect on current trends and possible future research areas.
... Other work explores the use of neural networks as efficient diagnostic tools for estimating the percentage of stator winding shorted turns on three-phase IMs. The implementation was done in MATLAB under different load conditions [45]. From the previous discussion of the works reported in the literature, it can be noted that data-driven, machine learning, and deep learning are techniques and schemes that provide reliable approaches for detecting ITSC. ...
... Some approaches are based on the data processing of the physical signals separately or considering a single signal without exploiting the potential of data fusion [44,45]. ...
The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.
... Other work explores the use of neural networks as efficient diagnosing tools for estimating the percentage of stator winding shorted turns on three-phase IMs. The implementation was done in MATLAB under different load conditions [45]. From the previous discussion of the works reported in the literature, it can be noted that data-driven, machine learning, and deep learning are techniques and schemes that provide reliable approaches for detecting ITSC. ...
Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.
... In, [20], stator windings short inter-turn fault modeling of IM in dq0 stationary reference frame is introduced and all parameters change due to fault are detailed. In, [21], a mathematical model was obtained in the dq0 reference frame for stator inter-turn short circuit fault in one phase, the developed model was simulated in MATLAB/SIMULINK environment for motors with different short-circuit percentages. In, [22,23], a dq0-based model approach in which the portion of short-circuit and the change of winding inductance in the stator is that to consider the leakage inductance to be proportional to square turns number was detailed just for short-circuit faults in one phase. ...
... To develop the Simulink model for stator short circuit, the current equations from the voltage matrix system in equation (13) can be rearranged in terms of currents, and equations (21) to (26) which are applicable in the Simulink environment are obtained as following expressions: ...
Owing to their robust structure, induction motors are preferred to be used under difficult working conditions. Therefore, various faults may occur in the motor due to unexpected forces during the operations. Obtaining the data through experimental methods by physically creating faults in the induction motors, and analyzing their behavior is not efficient in terms of cost and time for educational purposes. Considering the above negative situation, in this paper, mathematical models have been developed in the dq0 stationary reference frame expressing three-phase stator windings short-circuit fault and broken rotor bar fault in induction motors. The proposed models are faster as compared to the other analytical models in terms of computation due to the rotor position independence of the inductance matrix. The faulty induction motor mathematical models have been implemented in the MATLAB/SIMULINK software environment with detailed explanations of each faulty model’s subsystem. As a visual laboratory that can be used as an educational tool for the analysis of a three-phase faulty induction motor, a graphical user interface application has been developed in MATLAB/GUIDE, which allows users to simulate models from a single interface. As a case study, the behaviors of faulty induction motors in transient and steady states have been simulated in different severity scenarios of the faults. The park vector method has been used as a fault diagnosis approach to investigate fault types and the fault severity effects on the park vector pattern in each fault scenario. In addition, to observe the success of the developed Simulink toolboxes, they were used at Marmara University through courses in electrical machinery and evaluated by the graduated students at the end of the semester.
... Unbalance faults were identified using the Generalised Likelihood Ratio Test (GLRT). L. Maraaba et al. introduced Neural Networks (NNs) for analysing three phase Asynchronous Motor (AM) stator shorted turns [23]. The motor's output of electromechanical torque served as a fault indication. ...
This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN). The goal of the proposed technique is to detect and classify Asynchronous Motor faults at their early stages, under both normal and abnormal operating conditions. The proposed technique uses a recurrent neural network in two phases to identify and label interturn insulation concerns, with the first phase being utilised to establish whether or not the motors are healthy. In the second step, it discovers and categorises potentially dangerous interturn errors. The SSO approach is used in the second phase of the recurrent neural network learning procedure, with the goal function of minimizing error in mind. The proposed CSSRN technique simplifies the system for detecting and categorizing the interturn insulation issue, resulting in increased system precision. In addition, the proposed model is implemented in the MATLAB/Simulink, where metrics such as accuracy, precision, recall, and specificity may be analysed. Similarly, existing methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), and Salp Swarm Algorithm Artificial Neural Network (SSAANN) are utilised to evaluate metrics such as Root mean squared error (RMSE), Mean bias error (MBE), Mean absolute percentage error (MAPE), consumption, and execution time for comparative analysis.
... For the development of an efficient diagnosis system, the main point is the identification of the [12,13], vibration signal [14] and mechanical speed [15]. ...
The three-phase induction motor is well suited for a wide range of mobile drives, specifically for electric vehicle powertrain. During the entire life cycle of the electric motor, some types of failures can occur, with stator winding failure being the most common. The impact of this failure must be considered from the incipient as it can affect the performance of the motor, especially for electrically powered vehicle application. In this paper, the intern turn short circuit of the stator winding was studied using Fast Fourier transform (FFT) and Shor-Time Fourier transform (STFT) approaches. The residuals current between the estimated currents provided by the extended Kalman filter (EKF) and the actual ones are used for fault diagnosis and identification. Through FFT, the residual spectrum is sensitive to faults and gives the extraction of inter-turn short circuit (ITSC) related frequencies in the phase winding. In addition, the FFT is used to obtain information about when and where the ITSC appears in the phase winding. Indeed, the results allow to know the faulty phase, to estimate the fault rate and the fault occurrence frequency as well as their appearance time.
... In Ref. [35], fault features were extracted from the electromechanical torque obtained from the simulation model created for the detection of ISCF in the induction motor (IM). Torque-based statistical features were employed as input data for artificial neural networks and the turn fault severity was detected with an accuracy of 88% À99%. ...
Fault detection is an important issue for permanent magnet synchronous motors (PMSMs). In the initial stage, it is very crucial to detect stator winding inter-turn short-circuit failure, which is one of the most common types of faults. In this paper, a new approach based on electromechanical torque has been proposed to detect the stator inter-turn short circuit fault (ISCF) that occurs in surface-mounted permanent magnet synchronous motors (PMSMs). New fault signatures based on the torque signal that can be used in stator winding ISCF detection are tried to be found in the torque frequency distribution. Fast Fourier Transform (FFT) was used to extract the torque frequency components associated with the stator ISCF. It was found that the amplitudes of the 2nd and 4th harmonic components of the torque signal are distinctive features that can be used for stator winding ISCF detection in PMSM. With the proposed components of the 2nd and 4th harmonic of torque, an inter-turn fault can be easily detected at the initial stage. Both experimental results and simulation results for healthy and three different faulty states (2%, 12.5%, and 25% ISCF) at different load levels and different speeds are presented in this paper.
... As mentioned previously, AI algorithms are becoming quite popular for inter-turn fault diagnosis of rotating machines owing to their robustness and adaptation abilities. Table 1 summarizes the related and latest AI-based techniques used for stator inter-turn fault detection in induction machines (IM) (Najafi et al., 2020;Khanjani and Ezoji, 2021;Mahami et al., 2021;Rebouças Filho et al., 2018;Maraaba et al., 2018;Daisy et al., 2019;Rajamany et al., 2019;Skowron et al., 2020b;Skowron and Orłowska-Kowalska, 2020;Mejia-Barron et al., 2021;Wei et al., 2021). ...
Fault diagnosis in induction machines (IM) require effective detection at early stages to prevent permanent machine failure. This requires fast sensing of disturbances as well as efficient diagnosis methods. Conventional signal measuring techniques are usually intrusive, sensitive, with a low signal-to-noise ratio, and may show limited performance at incipient fault stages. Moreover, traditional machine learning (ML) diagnosis techniques based on handcrafted feature extraction methods suffer from limitations that can be overcome by deep learning (DL) with automatic feature extraction capabilities. In this paper, an online, non-intrusive method is proposed for fault diagnosis in a three-phase IM based on infrared imaging. The proposed method integrates three convolutional neural network (CNN)-based DL models (Inception, Xception, and MobileNet) to identify IM health status, fault types, and stator Inter-turn faults (ITF) location and severity. Deep features obtained from these CNNs are merged through Discrete Wavelet Transform (DWT) resulting in spatial-time-frequency features. This fusion reduces the input features' size and improves diagnostic accuracy. Merged features' length is further reduced using the Principal Component Analysis (PCA) where these reduced features are then used for classification. Compared to DL-based diagnosis methods introduced in previous work, the proposed method shows superior performance. First, it uses the IR images directly without the need for clustering and segmentation steps, thus saving time and effort. Moreover, ensembled DL is applied via combining the three CNNs benefits altogether rather than applying an individual DL method. Finally, unlike the huge features' number exhibited by previous studies, the proposed approach uses minimal features, thus reducing classification complexity and time while maintaining 100% classification accuracy.
