Faults In Induction Motors Research Articles

Hybrid Model-Based Fuzzy Logic Diagnostic System for Stator Faults in Three-Phase Cage Induction Motors

Hybrid Model-Based Fuzzy Logic Diagnostic System for Stator Faults in Three-Phase Cage Induction Motors

An Integrated Strategy for the Real-Time Detection and Discrimination of Stator Inter-Turn Short-Circuits and Converter Faults in Asymmetrical Six-Phase Induction Motors

An Integrated Strategy for the Real-Time Detection and Discrimination of Stator Inter-Turn Short-Circuits and Converter Faults in Asymmetrical Six-Phase Induction Motors

An Adversarial Multisource Data Subdomain Adaptation Model: A Promising Tool for Fault Diagnosis of Induction Motor Under Cross-Operating Conditions

Typical domain adaptation neural network that takes multi-source heterogeneous data as input usually achieves poor diagnostic accuracy in induction motor fault diagnosis under cross-operating conditions. Aiming at this problem, the present study proposes an adversarial multi-source data subdomain adaptation (AMDSA) model. This model encapsulates three types of modules: a shared feature extractor, a label predictor and a series of domain discriminators. The joint operation of the shared feature extractor and the domain discriminators is used to perform subdomain adaptation of different types of data for obtaining domain invariant features of multi-source heterogeneous data. The label predictor is employed to fuse these domain invariant features and realize label classification. The proposed model can solve the problem of multi domain adaptation in multi-source heterogeneous data through constructing a subdomain adaptation strategy and a feature fusion strategy. The effectiveness of AMDSA is verified by a series of diagnostic experiments on faulty induction motors under cross-operating conditions. The experimental results show that the average diagnostic accuracy of all cross-operating conditions reaches 97.62%.

An Overview on the Coupled Electrical Circuit Method Approach for Asynchronous Electric Drive Fault Modelling

The paper gives an overview of the approach to mathematical modeling of developing faults in induction motor by analytical models based on the method of coupled electrical circuit. Mathematical models that allow taking into account individual electrical and mechanical defects of an asynchronous electric drive, such as bearing defects, eccentricity, various types of developing inter-turn short circuits of the stator winding, defects in the rotor bars, defects in the voltage source inverter are presented. By using the given mathematical models on the unified methodological basis, a program complex in MATLAB environment for investigation of emergency and pre-emergency modes of frequency-controlled and unregulated AC drive has been created with an acceptable calculation time and computing resources over long time intervals. It can also be used to study the influence on the spectral composition of currents of both individual defects and their combinations. The selected results of research on the given programs are given. The research results are verified by field experiments and comparison with ISO 20958 Condition monitoring and diagnostics of machine systems.

Experimental Investigation of the Combined Fault: Mechanical and Electrical Unbalances in Induction Motors Based on Stator Currents Monitoring

Numerous studies examine the electrical and mechanical, internal and exterior problems of induction motors separately. However, this type of machines is also susceptible to several combined faults which affect it at the same time. The aim of this work is to study firstly the effects of the mass imbalance with the supply voltage unbalance faults in a combined state, and specifically its effects on the stator currents (distortion of currents waveform and augmentation of current unbalance factor (CUF)). The findings of this research were derived from experimental tests, which enabled us to produce the combined voltage and mass imbalance fault intentionally. The second purpose of this paper is to identify these irregularities. In the field of induction motor fault diagnosis, the stator current analysis techniques have shown to be quite successful and have gained widespread use. Therefore, we used the well-known method motor current signature analysis (MCSA). In addition, we have strengthened the research by using spectral analysis on the Park's components (Id and Iq). The obtained results demonstrate the efficacy of analyzing stator currents and Park components spectra (particularly the direct Park component (Id)) for detecting this kind of electrical and mechanical combined defects

Analysis of PWM Fault of Induction Motor with Finite Element Method

Bu makalede, asenkron motorun (ASM) etkin bir simülasyon platformunun geliştirilmesi amaçlanmıştır. Simülasyonda motor kontrolü, sürücü devresi ve ayrıca asenkron makineyi içeren birleştirilmiş bir model oluşturulmuştur. Bu birbirinden farklı modeller her biri farklı yazılım ortamlarında elde edilmiştir. Dolayısıyla bu çalışma, tüm bileşenleri tek bir ortak simülasyon platformu ortamına entegre eden ASM için gelişmiş bir modelleme ve simülasyon araçları sağlamayı amaçlamaktadır. Bu çalışmada, ASM Modeli Ansys-Maxwell kullanılarak Sonlu Elemanlar Analizi (SEA) yapılarak model oluşturulmuş, sürücü devresinin simülasyon modeli ise Ansys-Simplorer'da geliştirilmiş ve kontrol kısmı MATLAB-Simulink ortamında oluşturulmuştur. Bu modelin, gerçekçi bir simülasyon ortamı sağladığı elde edilen sonuçlarla doğrulanmıştır. Böylece oluşturulan bu birleşik simülasyon platformunun farklı sürücü bileşenlerinin arızalı durumları modellemek için kullanılabildiği görülmüştür. Burada, arızalı bir PWM invertörün etkileri incelenmiştir. Arızalı durumlar için ASM'nin kontrol sürücüsünün performansını incelemek için, ASM’nin davranışını ve sürücüyü analiz etmek için genel dinamik modelin ortak bir simülasyonu önerilmiştir. Bu çalışmada, eş-simülasyon gerçekleştirilmiştir; ayrıca, skaler kontrolün simülasyon sonuçları, önerilen SEA platformunun hassasiyetinin doğrulanması yapılmıştır

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals.

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time.

Detection and Classification of Multiple Faults of Induction Motors by Using Artificial Neural Networks

Asenkron motor arızalarının tespiti asenkron motorların bakımı için kritik bir konudur. Stator akımı analizi motor arızalarını tespit etmek için yaygın olarak kullanılan bir yöntemdir. Asenkron motor arızalarının tespitine yönelik çok sayıda çalışma bulunmaktadır. Ancak çoklu arıza tespiti ve sınıflandırılmasına yönelik sınırlı sayıda çalışma yapılmıştır. Bu çalışmada, 3 kW bilezikli bir asenkron motorun stator kısa-devre arızaları, kırık rotor çubuğu ile iç bilezik ve dış bilezik rulman arızalarının tespiti ve sınıflandırılması çok katmanlı geri yayılım algoritmasına sahip yapay sinir ağı (YSA) modelleri ile gerçekleştirilmektedir. Çalışma üç aşamada gerçekleştirildi. Birinci aşamada asenkron motor tekil arızalarla birlikte test edildi. Asenkron motor stator sargısı %1, %2, %3, %4 ve %5 oranında kısa-devre edilerek, rotorda üç çubuk kırılarak ve motorun yük tarafı rulmanının iç bileziğinde ve dış bileziğinde arızalar oluşturularak ayrı ayrı test edildi. İkinci aşamada motor, %3 ve %5 stator sargısı kısa-devre arızalarıyla birlikte üç çubuğu kırık rotor ile test edildi. Üçüncü aşamada ise asenkron motor %3 ve %5 stator sargısı kısa-devre arızası, üç çubuğu kırık rotor, iç bileziği ve dış bileziği arızalı rulman ile birlikte test edildi. Bütün testlerde motor tam yük altında çalıştırılmıştır. Sunulan yöntem ile çoklu arızaların tespiti ve sınıflandırılması gerçekleştirilmiştir. Yapılan çalışmada, çoklu arıza tespitinde en yüksek başarım oranı %87 olarak elde edildi. Elde edilen sonuçlarla sunulan yöntemin uygulanabilirliği gösterilmiştir.

A novel dataset and lightweight detection system for broken bars induction motors using optimizable neural networks

Broken rotor bars (BRBs) in induction motors (IMs) are a common kind of failure and one of the most difficult to detect since the induction motor continues to run properly in the absence of any evidence of a malfunction. There have been several approaches presented for identifying BRBs in IMs. However, in order to parameterize the current signal, they need tools with a high computational cost, making an online implementation of the appropriate monitoring system difficult. As a result, this paper proposes a novel AI-based approach to detect and classify BRBs faults in IMs using novel multi-class datasets from two three-phase, 400V induction motors; 2.2kW, two poles, 50 Hz, 24 stator slots and with 20,29 and 32 rotor bars numbers (M24Ns,(20,29,32)Nb), and 2.2kW, four poles, 60 Hz, 36 stator slots with 28 and 44 rotor bars numbers (M36Ns, (28,44)Nb) to help in the analysis of healthy and faulty IMs, as well as classification performance evaluation and benchmarking. The developed lightweight, intelligent, and autodetection system employs a self-configurable neural network model for BRBs in IM models. It provides four classification outputs: healthy, one-BRB, two-BRBs, and three-BRBs. The simulation results characterize the performance of the (M24Ns, (20,29,32) Nb) and (M36Ns, (28,44) Nb) IMs and the combined mode for both motors, demonstrating that the proposed approach is very effective in detecting and classifying BRBs, with a 99.8 % and a prediction time of 1.64 microseconds.

Real-Time Neural Classifiers for Sensor and Actuator Faults in Three-Phase Induction Motors

The main steps involved in a fault-tolerant control (FTC) scheme are the detection of failures, isolation and reconfiguration of control. Fault detection and isolation (FDI) is a topic of interest due to its importance for the controller, since it provides the necessary information to adjust and mitigate the effects of the fault. Generally, the most common failures occur in the actuator or in sensors, so this article proposes a novel model-free scheme for the detection and isolation of sensor and actuator faults of induction motors (IM). The proposed methodology performs the task of detecting and isolating faults over data streams just after the occurrence of the failure of an induction motor (IM), by the occurrence of either disconnection, degradation, failure, or connection damage. Our approach proposes deep neural networks that do not need a nominal model or generate residuals for fault detection, which makes it a useful tool. In addition, the fault-isolation approach is carried out by classifiers that differentiate characteristics independently of the other classifiers. The long short-term memory (LSTM) neural network, bidirectional LSTM, multilayer perceptron and convolutional neural network are used for this task. The proposed sensors’ and actuator’s fault detection and isolation scheme is simple. It can be applied to various problems involving fault detection and isolation schemes. The results show that deep neural networks are a powerful and versatile tool for fault detection and isolation over data streams.

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries. A stator inter-turn fault (SITF) is considered to be the most common electrical failure according to statistical studies. In this paper, an algorithm for the detection of an SITF is presented. It is based on one of the blind source separation techniques called principal component analysis (PCA). The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components. The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion. The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply. In addition, with a straightforward and low computational burden in the fault detection process, the proposed method is computationally efficient. To evaluate the performance of the proposed method, large numbers of practical and simulation scenarios are considered, and the results confirm the good performance, high degree of accuracy, and good convergence speed of the proposed method.

The Commercialization of Medium-Voltage Induction Motor Rotor Diagnostics Equipment: Helping Prevent In-Service Failures of Induction Motors and Processes

The R&D process for commercializing diagnostics equipment for detecting rotor faults in MV induction motors is described in this article. This was the result of a five-year collaborative effort among three independent parties: the equipment end user, equipment developer/supplier, and a university research group, where the tasks performed by each party are summarized in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> . The steps taken for successful development are given, along with a performance summary of the end product for which the first units have been in field use since 2017.

A Light Weight Multisensory Fusion Model for Induction Motor Fault Diagnosis

Fault diagnosis keeps an essential tool to ensure the safety and reliability of a motor system. Based on deep learning, fault diagnosis models constructed by mining historical fault data of equipment have received extensive attention. However, the high computational cost constrains the application of deep learning models for fault diagnosis, especially when coping with multisource data. Inspired by the model reduction and neural network structure automatic search method, this article proposed a light weight multisensory fusion model for induction motor data fusion and diagnosis. Inverted residual block and network architecture search technology are introduced to accelerate the training speed and prediction speed of the diagnostic model, so that the diagnostic accuracy is maintained at an acceptable level. The effectiveness of the proposed model is demonstrated through motor fault diagnosis experiments. Compared with other popular neural networks, the proposed method is capable of judging fault patterns accurately with shorter prediction time.

Inter-turn fault detection of induction motors using a method based on spectrogram of motor currents

Steady-state signatures seem more reliable than incipient fault signatures for detection of inter-turn short circuit (ITSC) fault in induction motors (IMs). An efficient detection approach is proposed based on the deviation of the histogram relevant to the time–frequency plane’s image of the steady-state modal current signal from the standard normal distribution. First, the misleading frequency components like 3rd, 5th, and 7th harmonics are excluded using Kalman Filter (KF). Then, from the time–frequency spectrogram of the signal, the converted gray-level image and its histogram are obtained. A considerable deviation of the histogram distribution from a normal distribution is observed in the case of ITSC fault, used for the detection. A cumulative index including normalized skewness and kurtosis of the histogram is used as the criterion of the detection. Finally, a novel threshold setting method based on Otsu’s threshold principle is proposed, which efficiently distinct faulty conditions from healthy operations. The required data for assessment of the approach is gathered from different experiments carried out on a test bench, subjected to various fault percentages and different load levels. The results confirm the effectiveness of the proposed methodology.

Diagnosis of Broken Rotor Bars in Six-Phases Squirrel Cage Induction Motor

Multiphase machines are increasingly used for reasons of reliability and power segmentation. This paper proposes to study a common example of these machines, six-phase squirrel cage induction motor. The study focuses on the broken bars fault when the motor is supplied by voltage source inverter. The originality of this work lies in the application of diagnostic techniques in a non-conventional machine such as six-squirrel cage induction motor. Therefore, this paper discusses the effect of the broken rotor bar fault in the induction six-phase squirrel cage motor. The drive of this motor requires a multiphase power supply. This is the reason why a six-phases inverter has been built to ensure the healthy or faulty operation of this motor. In order to study the rotor fault, a test bench contains several rotors with rotor broken bars. In order to detect this fault, Motor Current Signature Analysis (MCSA) has become the most common method in the field due to its simplicity, its low computation time, and remote monitoring capability this why this technique is used in this work. The experimental results show the interest and the efficiency of the technique used for the diagnosis of rotor bar faults in the six-phase squirrel cage induction motor.

Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

Motivation: This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, and ANN is used for classification using activation functions. Activation provides output by assigning the specific input with respect to the transfer function according to the nature and type of the activation function. Method: The faulty conditions are induced using MATLAB by adopting the motor current signature analysis (MCSA) method to achieve current signature signals of the healthy and faulty motors. Results: The DWT technique has been applied to obtain fault-specific features of the average continuously varying signal (RMS) and an average of the data points (mean) at levels 5, 7, 8, and 9, followed by ANN to classify the faults for condition monitoring. Utility: The utility of the results is to reduce unscheduled downtime in the industry, thus saving revenue and reducing production losses. This work will help provide support to ensure early indication of faults in induction motors under operating conditions, enabling in-service engineers to take timely preventive measures as part of the availability of resources in IoT-enabled systems. Application: Resource availability and cybersecurity are becoming vital in an environment that supports the Internet of Things (IoT) as the essential components of Industry 4.0 scenarios. The novelty of this research lies in the implementation of high contact resistance and rotor bar faults using DWT and ANN with different activation functions to achieve accuracy up to 98%.

Early Detection of Faults in Induction Motors—A Review

There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.

Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis.

The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.

Coupled Electromagnetic-Dynamic Modeling and Bearing Fault Characteristics of Induction Motors considering Unbalanced Magnetic Pull

Induction motors are complex energy conversion systems across the domains of dynamics, electricity, and magnetism. Most existing models mainly consider unidirectional coupling, such as the effect of dynamics on electromagnetic properties, or the effect of unbalanced magnetic pull on dynamics, while in practice it should be a bidirectional coupling effect. The bidirectionally coupled electromagnetic-dynamics model is beneficial to the analysis of induction motor fault mechanisms and characteristics. This paper proposes a coupled electromagnetic-dynamic modeling method that introduces unbalanced magnetic pull. By using the rotor velocity, air gap length, and unbalanced magnetic pull as the coupling parameters, the coupled simulation of the dynamic and electromagnetic models can be effectively realized. Simulation results for bearing faults show that the introduction of magnetic pull induces a more complex dynamic behavior of the rotor, which in turn leads to modulation in the vibration spectrum. The fault characteristics can be found in the frequency domain of the vibration and current signals. Through the comparison between simulation and experimental results, the effectiveness of the coupled modeling approach and the frequency domain characteristics caused by the unbalanced magnetic pull are verified. The proposed model can help to obtain a variety of information that is difficult to measure in reality and can also serve as a technical basis for further research on nonlinear characteristics and chaos in induction motors.

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.