Faults In Induction Motors Research Articles

Experimental Diagnosis of Broken Rotor Bar Faults in Induction Motors at Low Slip via Hilbert Envelope and Optimized Subtractive Clustering Adaptive Neuro-Fuzzy Inference System

Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not provide satisfactory results if it is applied directly to the stator current signal at low slip because a high-resolution spectrum is required to separate the different components of the frequency. To address this problem, this paper proposes an efficient method based on a Hilbert fast Fourier transform (HFFT) approach, which is used to extract the envelope from the stator current using the Hilbert transform (HT) at low slip. Then, the stator current envelope is analyzed using the fast Fourier transform (FFT) to obtain the amplitude and frequency of the particular harmonic. These data were recently collected and selected as BRB fault features and were employed as adaptive neuro-fuzzy inference system (ANFIS) inputs for BRB fault autodiagnosis and classification. To identify the BRB defect by determining the number of broken bars in the rotor, two ANFIS models are proposed: ANFIS grid partitioning (ANFIS-GP) and ANFIS-subtractive clustering (ANFIS-SC). To validate the effectiveness of the proposed method, three different motors were used during experiments under various loads; the first was with one broken bar, the second was with two adjacent broken bars, and the third was a healthy motor. The obtained results confirmed the effectiveness and the robustness of the proposed method, which is based on the combination of HFFT-ANFIS-SC to diagnose the BRB faults and quantify the number of broken bars under different load conditions (under low and high slip) precisely with minimal errors (this method had an MSE of 10-14 and 10-7 for the RMSE) compared to the method based on the combination of HFFT-ANFIS-GP.

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

An Infrared Thermal Image Few-Shot Learning Method Based on CAPNet and Its Application to Induction Motor Fault Diagnosis

The induction motor is widely used for providing the running power of rotating machinery. Its fault diagnosis is significant to ensure the operation safety of rotary machinery. Infrared thermal image analysis based on deep learning has attracted the attention of many researchers due to its advantages in non-destructive and space locations. However, obtaining sufficient high-quality thermal image samples in practical applications is relatively difficult. Developing few-shot learning models is significant for extending the engineering application of thermal image analysis. The existing models of few-shot learning often neglect the spatial information extraction of images at the feature extraction module, which limits the performance of models. In this paper, a new prototypical network with coordinate attention (CAPNet) for fault diagnosis of three-phase induction motor is proposed. The coordination attention feature extraction module (CAFEM) is designed by the coordinate attention block and the convolution blocks to obtain the spatial relativities between feature maps. The designed CAFEM can inhibit harsh requirements on the amount of data, which greatly enhances the CAPNet’s ability to mine information. Moreover, the features exacted of fault are identified by the metric classifier. The performance of the CAPNet method is validated by experimental analysis of thermal images of a three-phase induction motor. The superiority of the proposed method is demonstrated in comparison with six other state-of-the-art algorithms in few-shot learning.

Infrared Thermography Smart Sensor for the Condition Monitoring of Gearbox and Bearings Faults in Induction Motors.

The monitoring of machine conditions is very important from the viewpoints of productivity, economic benefits, and maintenance. Several techniques have been proposed in which sensors are the key to providing relevant information to verify the system. Recently, the smart sensor concept is common, in which the sensors are integrated with a data processing unit executing dedicated algorithms used to generate meaningful information about the system in situ. Additionally, infrared thermography has gained relevance in monitoring processes, since the new infrared cameras have more resolution, smaller dimensions, reliability, functionality, and lower costs. These units were firstly used as secondary elements in the condition monitoring of machines, but thanks to modern techniques for data processing, the infrared sensors can be used to give a first, or even a direct, diagnosis in a nonintrusive way in industrial applications. Therefore, in this manuscript, the structure and development of an infrared-thermography-based smart sensor for diagnosing faults in the elements associated with induction motors, such as rolling bearings and the gearbox, is described. The smart sensor structure includes five main parts: an infrared primary sensor, a preprocessing module, an image processing module, classification of faults, and a user interface. The infrared primary sensor considers a low-cost micro thermal camera for acquiring the thermal images. The processing modules and the classification module implement the data processing algorithms into digital development boards, enabling smart system characteristics. Finally, the interface module allows the final users to require the smart sensor to perform processing actions and data visualization, with the additional feature that the diagnosis report can be provided by the system. The smart sensor is validated in a real experimental test bench, demonstrating its capabilities in different case studies.

Inter-Turn Short-Circuit Faults Detection and Monitoring of Induction Machines Using WPT-Fuzzy Logic Approach Based on Online Condition

This paper proposes an efficient fuzzy logic-based fault detection scheme for diagnosing the inter-turn short-circuit (ITSC) faults in induction motors (IMs). The proposed approach utilizes the fast Fourier transforms (FFTs) and wavelet packet transform (WPT) for this detection of fault. To improve the efficiency and secure the operation, the proposed approach is detecting the fault in online manner. The WPT is utilized to extract the stator current signal into time-frequency domain characteristics. The variation in the amplitude of the vibration spectrum at different characteristic frequencies by FFT is utilized to identify the stator ITSC. The vibration signal is dignified by a MEMS accelerometer. The performance of the fuzzy logic fault detector (FLFD) for online condition is monitored with stator current, vibration and input speed. The performance of the proposed approach is performed at MATLAB/Simulink working site, and then the performance is compared to other existing works. The accuracy, precision, recall and specificity of the proposed approach are analyzed. Similarly, the statistical measures like root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (MBE) and consumption time are analyzed.

Intelligent detection of stator and rotor faults of induction motor based on improved backstepping sliding mode observer

Intelligent detection of stator and rotor faults of induction motor based on improved backstepping sliding mode observer

Fault Diagnosis and Tolerant Control for Three-Level T-Type Inverters

This paper proposes a fault diagnosis system for inverters based on a cerebellar model articulation controller (CMAC). First, a three-level T-type inverter was implemented and used to create a three-level T-type inverter test environment for measuring the output voltage waveforms of faulty power transistors on the main inverter circuit under different output frequencies. The measured waveforms were processed using a fast Fourier transform (FFT) algorithm to create frequency spectrum diagrams and extract the characteristic spectra of corresponding faulty switches. Then, the associations of the spectra were determined and applied as training data for the CMAC to detect the positions of the faulty power transistors. The test results demonstrated that the proposed induction motor fault diagnosis system is capable of fast algorithm, requires less data to train, and has excellent accuracy of identification, with an error margin of ±5%. The detection results were then processed using a fault-tolerant controller (FTC) to enhance the reliability of the proposed system. Finally, some simulations and experimental results were conducted and analyzed to validate the feasibility of the proposed FTC system.

Diagnostic of Combined Mechanical and Electrical Faults in ASD-Powered Induction Motor Using MODWT and a Lightweight 1-D CNN

The early fault detection in the rotary electrical machines,such as induction motors (IMs), has been growing in modern industry. IMs have been widely used in industrial applications due to its easy installation, reliability, and low cost. However, the increasing usage of IMs also increases the need for timely maintenance in order to ensure their operation and a longer service life. This article proposes a new diagnosis methodology based on maximal overlap discrete wavelet transform and a lightweight 1-D convolutional neural network (CNN) architecture, in order to detect mechanical and electrical faults and their combination, in adjustable speed drive (ASD)-powered IMs. Specifically, single and combined faults were studied from the next: Outer raceway bearing (mechanical), turn-to-turn short-circuit, and phase-to-ground short circuit (electrical). The presented study was developed using current signals acquired from stators of IMs of 1 hp. The current signals are measured at powered conditions introduced by a power grid with a constant frequency at 60 Hz, and an ASD at three different frequencies. The proposed diagnostic methodology reaches more than 99% of accuracy.

A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions

In industry, electric motors such as the squirrel cage induction motor (SCIM) generate motive power and are particularly popular due to their low acquisition cost, strength, and robustness. Along with these benefits, they have minimal maintenance costs and can run for extended periods before requiring repair and/or maintenance. Early fault detection in SCIMs, especially at low-load conditions, further helps minimize maintenance costs and mitigate abrupt equipment failure when loading is increased. Recent research on these devices is focused on fault/failure diagnostics with the aim of reducing downtime, minimizing costs, and increasing utility and productivity. Data-driven predictive maintenance offers a reliable avenue for intelligent monitoring whereby signals generated by the equipment are harnessed for fault detection and isolation (FDI). Particularly, motor current signature analysis (MCSA) provides a reliable avenue for extracting and/or exploiting discriminant information from signals for FDI and/or fault diagnosis. This study presents a fault diagnostic framework that exploits underlying spectral characteristics following MCSA and intelligent classification for fault diagnosis based on extracted spectral features. Results show that the extracted features reflect induction motor fault conditions with significant diagnostic performance (minimal false alarm rate) from intelligent models, out of which the random forest (RF) classifier was the most accurate, with an accuracy of 79.25%. Further assessment of the models showed that RF had the highest computational cost of 3.66 s, while NBC had the lowest at 0.003 s. Other significant empirical assessments were conducted, and the results support the validity of the proposed FDI technique.

Comprehensive and Simplified Fault Diagnosis for Three-Phase Induction Motor Using Parity Equation Approach in Stator Current Reference Frame

In this paper, a complementary and simplified scheme to diagnose electrical faults in a three-phase induction motor using the parity equations approach during steady state operation bases on the stator current reference frame is presented. The proposed scheme allows us to identify the motor phase affected due to faults related to the stator side, such as current sensors, voltage sensors, and resistance. The results obtained in this work complement a detection system that uses the DQ model of the three-phase induction motor and parity equations focused on the synchronous reference frame, which can detect stator-side faults but cannot locate the affected phase. In addition, considering practical and operational aspects, the residual detection set obtained is simplified to three simple algebraic equations that are easy to implement. The simulation results using the PSIM simulation software and the experimental test allow us to validate the proposed scheme.

A Negative Sequence Current Phasor Compensation Technique for the Accurate Detection of Stator Shorted Turn Faults in Induction Motors

Stator faults are the most critical faults in induction motors as they develop quickly hence requiring fast online diagnostic methods. A number of online condition monitoring techniques are proposed in the literature to respond to such faults, including the signature analysis of the stator current, vibration monitoring, flux leakage monitoring, negative sequence components of voltage and current and sequence components monitoring based on the identification of asymmetrical behavior in a machine. Negative sequence components of voltage and current and sequence components monitoring are commonly considered for the identification of asymmetrical behavior of induction motors. Negative sequence current analysis is a sensitive technique for the detection of shorted turns as it directly measures the asymmetry produced by the fault. However, the technique is sensitive to other asymmetrical faults and disturbances, which can also produce negative sequence currents. These disturbances include sensor errors, inherent asymmetry and voltage unbalance. This paper provides a comprehensive investigation of the disturbances using a motor model along with experimental measurements under varying load conditions. Then, a new phasor compensation technique is explained to eliminate such disturbances effectively. This technique enables the accurate detection of even relatively small shorted turn faults, even at an early stage. The technique is tested experimentally, and a set of practical results are given to validate the methods developed.

Post‐fault operation strategy for single open‐phase fault in three‐phase induction motor drives

Single open-phase fault (SOPF) in three-phase induction motor (TPIM) drives leads to undesired performances and in some cases shutdown of the drive system. This research proposes a post-fault operation strategy based on direct rotor field-oriented control (RFOC) technique for wye-connected TPIM drives during SOPF. In the proposed control system, symmetrical and asymmetrical transformation matrices are utilized for stator voltage and current variables, respectively. In addition, an extended Kalman filter (EKF) is used for the rotor flux estimation, filtration of stator currents, and obtaining their time derivatives. The performance of the introduced technique including steady and transient states during different reference speeds is experimentally validated for a wye-connected TPIM drive considering SOPF. Additionally, the performance of the proposed strategy is experimentally compared with conventional RFOC strategies.

Diagnosis of induction motor faults using the motor current normalized residual harmonic analysis method

Induction motors are susceptible to many types of faults that can become catastrophic and cause production stoppages. To predict emerging faults and avoid an unexpected failure, an induction motor can be diagnosed using the proposed motor current normalized residual harmonic analysis method. This method is simple and efficient. It is based on a more sensitive fault signature and a single frequency of the harmonic for the different faults. It is based also on the analysis of residual harmonics, which can be measured between the normalized linear fast Fourier transform spectrum of the healthy motor current and that of the faulty motor current. The proposed method can mitigate the task of the fault detection system by monitoring the occurrence of a residual harmonic fixed at a precise frequency. This study focuses on the rotor fault of broken bars and inter-turn short circuits in stator windings. In-depth experimental results demonstrate the rapid detection of different faults at the characteristic frequency of 50 Hz. It is not necessary to conduct a wide spectral sweep to search each time for different faults that have variable characteristic frequencies depending on the type of fault. This method can be useful for diagnosing other types of faults such as eccentricities, bearings and magnetic circuits.

Geometric Analysis of Signals for Inference of Multiple Faults in Induction Motors.

Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA). This method couples the measured signals from the motor current and the triaxial accelerometer mounted on the induction motor chassis to the quaternion coefficients. The QSA calculates the quaternion rotation and applies statistics such as mean, variance, kurtosis, skewness, standard deviation, root mean square, and shape factor to obtain their features. After that, four classification algorithms are applied to predict motor states. The results of the QSA method are validated for ten classes: four single classes (healthy condition, unbalanced pulley, bearing fault, and half-broken bar) and six combined classes. The proposed method achieves high accuracy and performance compared to similar works in the state of the art.

Current and Stray Flux Combined Analysis for the Automatic Detection of Rotor Faults in Soft-Started Induction Motors

Induction motors (IMs) have been extensively used for driving a wide variety of processes in several industries. Their excellent performance, capabilities and robustness explain their extensive use in several industrial applications. However, despite their robustness, IMs are susceptible to failure, with broken rotor bars (BRB) being one of the potential faults. These types of faults usually occur due to the high current amplitude flowing in the bars during the starting transient. Currently, soft-starters have been used in order to reduce the negative effects and stresses developed during the starting. However, the addition of these devices makes the fault diagnosis a complex and sometimes erratic task, since the typical fault-related patterns evolutions are usually irregular, depending on particular aspects that may change according to the technology implemented by the soft-starter. This paper proposes a novel methodology for the automatic detection of BRB in IMs under the influence of soft-starters. The proposal relies on the combined analysis of current and stray flux signals by means of suitable indicators proposed here, and their fusion through a linear discriminant analysis (LDA). Finally, the LDA output is used to train a feed-forward neural network (FFNN) to automatically detect the severity of the failure, namely: a healthy motor, one broken rotor bar, and two broken rotor bars. The proposal is validated under a testbench consisting of a kinematic chain driven by a 1.1 kW IM and using four different models of soft-starters. The obtained results demonstrate the capabilities of the proposal, obtaining a correct classification rate (94.4% for the worst case).

Classification of motor faults based on transmission coefficient and reflection coefficient of omni-directional antenna using DCNN

The most commonly used electrical rotary machines in the field are induction machines. In this paper, we propose an antenna based approach for the classification of motor faults in induction motors using the reflection coefficient S11 and the transmission coefficient S21 of the antenna. The spectrograms of S11 and S21 is seen to possess unique signatures for various fault conditions that are used for the classification. To learn the required characteristics and classification boundaries, deep convolution neural network (DCNN) is applied to the spectrogram of the S-parameter. DCNN has been found to reach classification accuracy 93% using S11, 98.1% using S21 and 100% using both S11 and S21. The effect of antenna operating frequency, its location and duration of signal on the classification accuracy is also presented and discussed.

Application of integrated PCA and FIS approach to the selection of current and vibration signal features in mechanical fault classification of induction motor

The detection as well as analysis of faults in Induction Motor (IM) is prominent in the industrial process in recent decades, since it has been a demanding issue in industries to confirm the safe and reliable operations of IM. Though the electrical faults, mechanical faults and environmental faults cause damages in IM, as per Electric Power Research Institute (EPRI) statistical studies, the faults due to (i) rotor mass unbalance and (ii) rotor shaft bending substantially contribute 8-9% of the total motor fault. This present research work focuses on the issue of detecting and analysing the faults by studying the current and vibration data obtained from the three-phase squirrel cage IM under healthy and faulty conditions using the experimental workbench. It also depicts the development of a fault detection model for IM which comprises the integrated approach of Principal Component Analysis (PCA) and Fuzzy Interference System (FIS) and two level decision fuzzy measures. Besides, fuzzy integral data fusion technique has been used in this work for the improvement of diagnosing accuracy. The data acquired from the workbench system are first investigated through the PCA to extricate the appropriate features that provide the major information of collected data without reducing its dimensions. The projected data space using the principal components is non-deterministic for further synthesis process of fault classification. Hence, to classify the faults in IM, the obtained feature vectors from PCA are fed into FIS as an input and the classification performance is compared finally. The work experiment has been carried out under the healthy and different faulty conditions of motor and the proposed integrated approach is executed by using MATLAB.

Incipient Interturn Fault Detection and Severity Evaluation in Electric Drive System Using Hybrid HCNN-SVM Based Model

The importance of induction motor (IM) drives in industrial applications, especially in the renewable energy sector is unarguable due to their fast-dynamic response. Their monitoring is key to avoid downtime and economic losses. Even though a lot of research has been focused on incipient fault diagnosis in induction machines, early diagnosis in power electronic drive-fed machines is still a challenge. In this article, a novel two-level hybrid hierarchical convolution neural network with support vector machine (HCNN-SVM) is proposed for incipient interturn fault diagnosis of drive-fed machines. The first level of the proposed structure is intended to recognize the pattern of interturn fault in drive-fed IM, while the second level is developed to identify the fault severity. First, the effective features are extracted automatically using the shared layers of HCNN for fault diagnosis and fault severity evaluation at the same time. The features obtained using the HCNN are suitably used to train SVM for classification. Comparison of the results with the existing architectures, namely, HCNN and SVM, show the effectiveness of proposed hybrid method. The experimental results show that the hybrid HCNN-SVM is fast and highly accurate in identifying the interturn fault pattern and evaluating its severity.

Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation

In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forward, backpropagation artificial neural network used as a classifier. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among five induction motor conditions, i.e., a motor in good physical condition (HLT), a motor with one broken rotor bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with damage on the bearing outer race (BRN), and a motor with an unbalanced mechanical load (UNB).

New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection

Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these signals of the motor were selected to test the proposed model. The induction motor was assessed in a laboratory under healthy, mechanical, and electrical faults with different loadings. In this study a new hybrid model was developed using the collected signals, an optimal features selection mechanism is proposed, and machine learning classifiers were trained for fault classification. The procedure is to extract some statistical features from the raw signal using Matching Pursuit (MP) and Discrete Wavelet Transform (DWT). Then, the Invasive Weed Optimization algorithm (IWO)-based optimal subset was selected to reduce the data dimension and increase the average accuracy of the model. The optimal subset of features was fed into three classification algorithms: k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), which were trained using k-fold cross-validation to distinguish between the induction motor faults. A similar strategy was performed by applying the Genetic Algorithm (GA) to compare with the performance of the proposed method. The suggested fault detection model’s performance was evaluated by calculating the Receiver Operation Characteristic (ROC) curve, Specificity, Accuracy, Precision, Recall, and F1 score. The experimental results have proved the superiority of IWO for selecting the discriminant features, which has achieved more than 99.7% accuracy. The proposed hybrid model has successfully proved its robustness for diagnosing the faults under different load conditions.