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Induction motors are robust and cost effective; thus, they are commonly used as power sources in various industrial applications. However, due to the characteristics of induction motors, industrial processes can stop when motor failures occur. Thus, research is required to realize the quick and accurate diagnosis of faults in induction motors. In t...
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... experimental laboratory setup for data collection and induction motor fault diagnosis is shown in Figure 1, and Figure 2 shows the block diagram of Figure 1. The induction motor simulator comprised an induction motor in normal, rotor fault, and bearing fault states. ...
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... experimental laboratory setup for data collection and induction motor fault diagnosis is shown in Figure 1, and Figure 2 shows the block diagram of Figure 1. The induction motor simulator comprised an induction motor in normal, rotor fault, and bearing fault states. ...
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... addition, all data should only be used once for model validation [46]. In this study, K was set to 2, as in Figure 10. ...
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... GUI for the proposed induction motor fault diagnosis technique is shown in Fig- ure 11. The GUI was implemented using LabView (National Instruments, TX, USA). ...
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... GUI was implemented using LabView (National Instruments, TX, USA). In Figure 11a, the waveform graph on the left shows the vibration data obtained from the induction motor in real time, where the horizontal axis represents time, and the vertical axis represents amplitude. The block diagram of the fault diagnosis GUI implemented through LabView is shown in Figure 11b. ...
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... Figure 11a, the waveform graph on the left shows the vibration data obtained from the induction motor in real time, where the horizontal axis represents time, and the vertical axis represents amplitude. The block diagram of the fault diagnosis GUI implemented through LabView is shown in Figure 11b. The diagnosis button was used to perform fault diagnosis on the induction motor. ...
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... the optimal SVM and CNN models outperformed the MNN, GBM, and XGBoost models. The graphs in Figure 12 show the failure diagnosis results obtained via the crossvalidation of the SVM, MNN, CNN, GBM, and XGBoost models. In the graph, the horizontal and vertical axes represent the number of data samples and the induction motor state, respectively. ...
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... confirmed the diagnosis accuracy in Table 12 and the computation speed in Table 13; we found that SVM and CNN had the best diagnostic accuracy and XGBoost had the fastest computation speed. Figure 13a-c show the induction motor fault diagnosis results using the GUI. As can be seen, the LED of the GUI was turned on in accordance with the state of the induction motor. ...
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... This diversity suggests that the occurrence of landslides is influenced by a multitude of factors, and it is unlikely that all contributing elements are as yet identified. On one hand, the complexity of the interrelations among various factors necessitates the consideration of numerous evaluation criteria to establish a robust susceptibility framework [8,9]. However, certain limitations inherent in machine learning models, as previously noted, may obscure these correlations and fail to yield optimal outcomes [10,11]. ...
Precisely assessing the vulnerability of landslides is essential for effective risk assessment. The findings from such assessments will undoubtedly be in high demand, providing a solid scientific foundation for a range of critical initiatives aimed at disaster prevention and control. In the research, authors set the ancient core district of Sichuan Moxi Ancient Town as the research object; they conduct and give the final result of the geological survey. Fault influences are commonly utilized as key markers for delineating strata in the field of stratigraphy, and the slope distance, slope angle, slope aspect, elevation, terrain undulation, plane curvature, profile curvature, mean curvature, relative elevation, land use type, surface roughness, water influence, distance of the catchment, cumulative water volume, and the Normalized Vegetation Index (NDVI) are used along roads to calculate annual rainfall. With the purpose of the establishment of the evaluation system, there are 17 factors selected in total. Through the landslide-susceptibility assessment by the coupled models of DNN-I-SVM and DNN-I-LR nine factors had been selected; it was found that the Area Under the Curve (AUC) value of the Receiver Operating Characteristic Curve (ROC) was high, and the accuracy of the model is relatively high. The coupler, DNN-I-LR, gives 0.875 of an evaluation accuracy of AUC, higher than DNN-I-SVM, which yielded 0.860. It is necessary to note that, in this region, compared to the DNN-I-SVM model, the DNN-I-LR coupling model has better fitting and prediction abilities.
... Let n denote the total number of weak learners, f i (x) represent the prediction of the ith weak learner given input x, γ be the regularization parameter, and Ω( f j ) be the regularization term. XGBoost was employed as one of the models for diagnostic analysis, demonstrating its efficacy in promptly and accurately detecting motor problems [31]. The results obtained from stratified K-fold cross-validation demonstrate that XGBoost is suitable for practical implementation in fault diagnosis for induction motors. ...
... The results obtained from stratified K-fold cross-validation demonstrate that XGBoost is suitable for practical implementation in fault diagnosis for induction motors. In addition, a user-friendly graphical interface was created to simplify the execution of the diagnostic technique [31]. ...
This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.
... The acquired thermal images can be compared under the healthy and faulty conditions in the induction motor. Besides, the performance of the proposed method is compared with existing methods ANN, CNN, and SVM [27] in terms of Accuracy, Sensitivity and Specificity, error rate, and false classification ratio. Finally, the efficiency of the healthy and faulty motor is compared through finite element analysis. ...
... If labeled data for the operation under faulty conditions are available, supervised machine learning can be used to classify data into different operation modes. There are several appropriate methods for this classification, including neural networks, support vector machines [4], principal component analysis [5], and decision trees [6]. The effectiveness of fault-type differentiation methods is heavily influenced by the features used. ...
This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.
... Therefore, it is necessary to have a technology that can timely detect the ITSC fault and take appropriate measures to ensure the safe operation of the equipment. The former fault detection methods, which were based on user reviews, required monitoring the visual and auditory information gathered from the equipment to assess the motor's health [3]. However, this approach incurred substantial costs in terms of human labor and time. ...
... However, this approach incurred substantial costs in terms of human labor and time. Currently, the main researched fault detection technologies include motor current signature analysis (MCSA) [4][5][6][7][8], vibration analysis [3,9,10], acoustic signal analysis [11][12][13], and thermal imaging analysis [14][15][16]. Jung et al. [4] proposed an online diagnostic method for current signature analysis based on advanced signal-and data-processing algorithms, which successfully achieved the diagnosis of three types of faults, namely rotor misalignment, stator winding short circuit, and bearing defects, in induction motors. ...
... Ciszewski et al. [7] proposed a current feature analysis diagnosis method based on higher-order spectral technology, which effectively identified bearing faults in induction motors. Kim et al. [3] proposed a fault diagnosis method based on vibration signals, and utilized various machine learning models to achieve the classification of motor health, rotor faults, and bearing faults. Kafeel et al. [8] proposed a fault detection system based on vibration signal analysis, which utilized empirical mode decomposition for multi-domain feature extraction and achieved satisfactory results through experiments. ...
Inter-turn short circuit (ITSC) is a common fault in induction motors. However, it is challenging to detect the early stage of ITSC fault. To address this issue, this paper proposes an ITSC fault detection method for three-phase induction motors based on start-up current envelope energy. This approach uses Akima interpolation to calculate the envelope of the measured start-up current of the induction motor. A Gaussian window weighting is applied to eliminate endpoint effects caused by the initial phase angle, and the enveloping energy is obtained using the energy formula as the fault feature. Finally, by combining this with the support vector machine (SVM) classification learner, fault detection of ITSC in induction motors is achieved. The experimental results show that the average accuracy of this method reaches 96.9%, which can quickly and accurately detect ITSC faults in asynchronous motors and determine the severity of the faults. Furthermore, the average accuracy of SVM in detecting early ITSC faults under no-load conditions is 98.8%, which is higher than other classification learners, including LR, KNN, and NN. This study provides a new idea for induction motor fault detection and can contribute to induction motor maintenance.
... in industries because of their simpler construction, strength, reliability, cost-effectiveness, and a wide range of applications [2]. However, deformation and wear of the motor parts due to long term service in harsh working conditions can reduce the life expectancy and performance of TIM [3]. The motors can suffer from stator [4], rotor [5] or bearing faults [6], thereby decreasing process safety. ...
The predictive health maintenance techniques identify the machine faults by analyzing the data collected by low-cost sensors assuming that sensors are free from any faults. However, aging and environmental condition cause sensors also be faulty, leading to incorrect interpretations of the collected data and subsequently resulting in erroneous machine health predictions. To mitigate this problem, this paper proposes a hybrid model that can differentiate between sensor and system faults. The data used for training the model is collected from a power system hardware setup by experimental procedures. A Convolutional Neural Network (CNN) model is used to extract optimized features from the raw data automatically, which are then fed to the XGBoost model for sensor and machine fault isolation with an overall accuracy of 98.15%. The data having sensor fault was then fed to a Deep Autoencoder (DAE), which eliminated the sensor fault components and reconstructed the data with an average RMSE of 0.0576. Thereafter, the corrected signal was used to detect the system fault using the hybrid CNN-XGBoost model with 99.77% accuracy. Therefore, by isolating the sensor faults, the proposed technique establishes better confidence in predictive maintenance. Further, Explainable AI has been utilized to interpret the model prediction in human-understandable terms in order to increase trustworthiness.
... Our preliminary experiments explored logistic regression and support vector machines (SVM), but these methods encountered limitations in handling the nonlinear relationships and variations present in the dataset [34,35]. Consequently, we transitioned to exploring artificial neural networks (ANNs), which demanded a substantial and diverse dataset to capture relevant features and learning patterns optimally [36]. ...
Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.
... Existing research in fault detection for induction machines has explored several diagnostic techniques. Studies have focused on detecting faults in both stator and rotor windings of doubly fed induction machines (DFIMs) [18][19][20][21][22][23]. Some researchers have utilized neural-network-based modeling of vibration spectra to address electrical faults in the stator winding [24]. ...
This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase “a” of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs.
... Examples of industrial machineries which attracted the attention of the FDI research in the last decades are machineries in industrial processes, manufacturing processes and theoretical processes ( [25][26][27][28][29]), e.g., motors ( [30][31][32][33][34][35][36][37][38][39]) and pumps ( [40][41][42][43][44][45][46][47]). In [25], PCA is exploited together with the expectation formulas of T 2 and squared prediction error statistics in order to achieve the detectability conditions of different faults in a simulated double-effective evaporator process. ...
... A three-step algorithm is used, based on data acquisition, eigenvector/eigenvalue computation, and the report of the extent of a fault. In [35], an induction motor simulator with normal, rotor failure, and bearing failure states is designed; the simulated data are exploited for failure diagnosis through different techniques, e.g., neural networks and machine learning models. In [36], fault diagnosis of electric motors is addressed. ...
In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.
... A new approach to the diagnostics of induction motors has been developed by the authors [66]. This approach is aimed at improving the reliability of electric vehicles. ...
... The issue of reliability of electric vehicles, especially motor controllers is extremely important, as shown by the work of previous authors [66]. It is known that power electronic components of the controller are less reliable than mechanical components of other parts of the electric car. ...
Modern power and transportation systems are subject to high requirements for reliability and performance in performing their specified functions. At the same time, these requirements are constantly increasing with the increasing complexity of technology and the introduction of electronics and computer technology into its structure. This is fully applicable to energy and transportation infrastructure, including electric vehicles. The complexity of the systems and increasing requirements for them have led to the fact that the problem of increasing their operational reliability has acquired great importance. The article presents a review of methods and justification of ensuring a high level of reliability and serviceability of technical systems as one of the most important tasks in the creation and operation of complex systems, such as modern energy and transportation systems. It is shown that a significant reserve in solving the problem of increasing the reliability and performance of technical systems is the information on failures and malfunctions of these systems obtained from the field of operation. The methodology of collection and processing of statistical information on failures of vehicles described by different distribution laws is outlined.
