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In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and othe...
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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction , high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in...
The diagnosis of misalignment plays a crucial role in the area of maintenance and repair since misalignment can lead to expensive downtime. To address this issue, several solutions have been developed, and both offline and online approaches are available. However, online strategies using a small number of sensors show a higher false positive rate t...
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... It must be highlighted that the DC generator represents a low mechanical load, entailing around 20% of the nominal load. The configuration of the low-level load is important because it can produce a downloading effect, which causes misleading fault detection, which is a challenge for the diagnostic in classic methodologies [40,41]. Thus, the entropy analysis is explored as an alternative in fault analysis under unfavorable conditions of low load. ...
The induction motors found in industrial and commercial applications are responsible for most of the energy consumption in the world. These machines are widely used because of their advantages like high efficiency, robustness, and practicality; nevertheless, the occurrence of unexpected faults may affect their proper operation leading to unnecessary breakdowns with economic repercussions. For that reason, the development of methodologies that ensure their proper operation is very important, and in this sense, this paper presents an evaluation of signal entropy as an alternative fault-related feature for detecting faults in induction motors and their kinematic chain. The novelty and contribution lie in calculating a set of entropy-related features from vibration and stator current signals measured from an induction motor operating under different fault conditions. The aim of this work is to identify changes and trends in entropy-related features produced by faulty conditions such as broken rotor bars, damage in bearings, misalignment, unbalance, as well as different severities of uniform wear in gearboxes. The estimated entropy-related features are compared to other classical features in order to determine the sensitivity and potentiality of entropy in providing valuable information that could be useful in future work for developing a complete methodology for identifying and classifying faults. The performed analysis is applied to real experimental data acquired from a laboratory test bench and the obtained results depict that entropy-related features can provide significant information related to particular faults in induction motors and their kinematic chain.
... Because it can address solutions to nonlinear models without knowing anything about their actual structure and provide better and faster response in the identification of motor faults [8][9][10][11][12]. The artificial neural network (ANN) [13], which excels in the field of pattern recognition, is the most widely utilized AI approaches for machine fault diagnosis. The entropy-based approach and its extensions have become increasingly widespread and it is employed recently for identifying rotary system problems. ...
Electrical equipment plays a vital role in industry. Among various electrical equipment, induction motors are quite commonly used in many industrial applications. One of the most common faults that occurs in induction motors is bearing fault. In this article, bearing fault is diagnosed in an induction motor using vibration signals with the help of a simple Artificial Intelligence (AI)-based model. Because, the vibration signals are not dependent on the motor type, simple to measure, cost effective and yields good results. In the proposed system, accurate prediction of bearing condition is carried out using Bayesian optimization-based ensemble classifier (BOEC). The performance of the BOEC-based bearing fault diagnosis system is compared with other conventional techniques and the comparison results confirm the superior performance of the proposed system. Also, the accuracy obtained from the BOEC-based bearing fault diagnosis system is 99.97%. To verify the effectiveness of the proposed system, a hardware prototype is set up in the laboratory and bearing conditions of various induction motors are analyzed.
... However, motor current signature analysis (MSCA) is one of the well-known methods used in the industrial environment for online monitoring, the wrong indication of fault becomes the major issue in this method that needs to be addressed [4]. The modelbased approach involves mathematical design to detect the fault in IMs while, the knowledge-based approach includes machine learning methods for both offline and online applications [3]- [5]. ...
... Various machine learning (ML) based techniques have been used in the literature. The knowledge-level modeling-based approach is used for broken rotor bar fault recognition using an artificial neural network [5]. The induction motor under direct torque control (DTC) is subjected to a rotor broken fault and is detected by ANN using Hilbert transform [6]. ...
... It is critical to keep I-Ms healthy to ensure that many industries are running correctly. Nevertheless, numerous faults occur regularly in IMs due to challenging operative situations, regular wear and tear, enduring and excessive loads, and unplanned circumstances [1][2][3][4]. Moreover, electrical machines (EM) are sensitive to a wide variety of malfunctions. ...
Induction Motors (I-M) aim to enhance interface technologies for more safety, reliability, productivity, and greener operations. In addition, malfunction monitoring functionalities are embedded into the system to detect impending faults and predict their consequence on the system's future actions using fault diagnosis techniques. This article broadens the scope of their investigation on current trends in fault detection and diagnosis of induction motors (CT-FDD) of I-M. In this direction, Modern applications, in particular, depend heavily on the rapid and accurate diagnosis of machinery malfunctions, which leads to increased productivity and reduced downtimes. It is worth mentioning that Artificial Intelligence (A-I) is a powerful technique for enhancing the capacity of I-M fault diagnosis, notably during the upkeep decision-making process. With this aim in mind, this article highlights the signatures of failure, including analysis of current motor signatures, voltage signatures, and acoustic and vibration analysis. In this direction, Actual stages in the design process of a fully automated CT-FDD system, such as system information processing, data capture, information theory, fault classification, and repair selection actions, are explained by the variation to provide an overview of the current state of CT-FDD.
... It is critical to keep I-Ms healthy to ensure that many industries are running correctly. Nevertheless, numerous faults occur regularly in IMs due to challenging operative situations, regular wear and tear, enduring and excessive loads, and unplanned circumstances [1][2][3][4]. Moreover, electrical machines (EM) are sensitive to a wide variety of malfunctions. ...
Induction Motors (I-M) aim to enhance interface technologies for more safety, reliability, productivity, and greener operations. In addition, malfunction monitoring functionalities are embedded into the system to detect impending faults and predict their consequence on the system's future actions using fault diagnosis techniques. This article broadens the scope of their investigation on current trends in fault detection and diagnosis of induction motors (CT-FDD) of I-M. In this direction, Modern applications, in particular, depend heavily on the rapid and accurate diagnosis of machinery malfunctions, which leads to increased productivity and reduced downtimes. It is worth mentioning that Artificial Intelligence (A-I) is a powerful technique for enhancing the capacity of I-M fault diagnosis, notably during the upkeep decision-making process. With this aim in mind, this article highlights the signatures of failure, including analysis of current motor signatures, voltage signatures, and acoustic and vibration analysis. In this direction, Actual stages in the design process of a fully automated CT-FDD system, such as system information processing, data capture, information theory, fault classification, and repair selection actions, are explained by the variation to provide an overview of the current state of CT-FDD.
... Bearing-related faults account for the vast majority of failure modes (52.5%). When the fault with the bearings and the faults with the stator are considered together, they account for more than 87.5% of the overall faults that are already present [16]. However, faults that are related to bearings are typically caused by mechanical and vibrational issues [17]. ...
... A separate type of measurement locations was supposed during certain positions to monitor the behavior of each electrical machine in Bearing-related faults account for the vast majority of failure modes (52.5%). When the fault with the bearings and the faults with the stator are considered together, they account for more than 87.5% of the overall faults that are already present [16]. However, faults that are related to bearings are typically caused by mechanical and vibrational issues [17]. ...
... This exposes some of the problems. It has the potential to alter the characteristics of a variety of features, which could lead to output impedance drops in almost the same path in the powerline network [16]. Through all the electrical charge reflection hypothesis, a defective signal can conveniently be propagated to neighboring motors at the given power frequency. ...
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.
... Rotor failures of almost account for 5-10% of the entire catastrophes [63][64][65]. Mostly, a squirrel-cage rotor is used, it is composed of aluminum or solid copper bars and are short by the end rings [38]. There are two types of cage rotors: cast and fabricated. ...
The induction motor is one of the essential components of the industry. In industrial applications, the controllability, protection, and reliability of induction motors are of major concern to reserve energy. Thus, condition monitoring of an induction motor is critical for improving the unit's reliability and, as a result, reducing downtime, labor, reducing energy wastage. Induction motor stator winding and bearing failures account for 37% and 41% of failures, respectively. As the sophisticated controls rely on parameters of the motor, while the protection and reliability depend on continuous and accurate monitoring of the health of the motor. This paper covers the state of the art of parameter estimation and condition monitoring of induction motors in order to help out the industry to minimize energy wastage. In order to assist the exact operation of an induction motor, initially, the fundamentals, structure, and model of an induction motor are explained. Further, this paper covers fault diagnoses that are capable of finding the symptoms of motor failure through the state of the art of parameter estimation. In addition, the medium for the root cause of an induction motor failure is described. Finally, the paper is concluded with what has been done already, the knowledge gap, and the potential of future research.
... The literature is filled with potential FDM systems [4]. Unfortunately, the majority of the FDM systems proposed by researchers make use of supervised learning models [5], [6], [7] and they suffer from certain flaws. In the case of supervised systems, manual data collection is required. ...
Predictive maintenance (PdM) systems have the potential to autonomously detect underlying motor issues at early stages. Although many such systems have been proposed up to data, they have yet to be implemented. Most of these methods, which are based on supervised learning, require hours of manual data collection and annotation. Furthermore, they are mostly made to tackle a single instead of the multiple motor issues that may occur and are unable to adapt to varying motor speed and load conditions. Thus, they are not viable for industrial implementation. Therefore, this paper presents an unsupervised LSTM autoencoder-based anomaly detection system for electric motors. It analyzes the vibration and current consumption data from motors to detect anomalies, which is sufficient to account for the various motor defects. The system comes with a variety of features that allows users to autonomously collect data, train models and deploy models. In addition to that, users can remotely keep track of the motor’s conditions. To test the system, a hardware test bench using a stepper motor is made to simulate defective conditions. The LSTM Autoencoder-based anomaly detection system is described step-by-step in this paper.
... Owing to the continuous severe mechanical stress, induction motors are prone to several failure modes, including mechanical and short circuit faults [1]. Other reasons that contribute to the likelihood of motor's faults include manufacturing defects, inappropriate installation, and other operational factors such as overloading, thermal stress, unbalanced voltage supply, insulation damage, and deterioration of grounding connection [2]. According to an IEEE statistical survey conducted on motors of sizes larger than 200 HP, bearing faults contribute the highest percentage (41%), followed by stator winding faults, which represent 37% of the faults taking place in rotating machines, as shown in Figure 1 While another survey conducted by the Electric Power Research Instit the reliability of motors less than 200 HP shows slight statistical differences survey, the likelihood of faults within the stator winding is still substanti quently, it is required to adopt a reliable tool to detect motor degradation a to avoid any potential consequences. ...
The industry has widely accepted Frequency Response Analysis (FRA) as a reliable method to detect power transformers mechanical deformations. While the FRA technique has been recommended in recent literature as a potential diagnostic method to detect internal faults within rotating machines, detailed feasibility studies have not been fully addressed yet. This paper investigates the feasibility of using the FRA technique to detect several short circuit faults in the stator winding of three-phase induction motors (TPIMs). In this regard, FRA testing is conducted on two sets of induction motors with various short circuit faults. Investigated faults include short circuits between two phases, short circuit turns within the same phase, phase-to-ground, and phase-to-neutral short circuit. The measured FRA signatures are divided into three frequency ranges: low, medium, and high. Several statistical indicators are employed to quantify the variation between faulty and healthy signatures in each frequency range. Experimental results attest the feasibility of the FRA technique as a diagnostic tool to detect internal faults in rotating machines, such as induction motors.
... Interest in the use of Artificial Iintelligence (AI) techniques in the process of fault diagnosis is also recently growing up. Because of its superior pattern recognition capabilities, the artificial neural network (ANN) is the most widely used AI technology [9]. It can also represent non-linear models without any knowledge of the really structure in a short amount of time, which resulting in promising outcomes in defect diagnostics [10]. ...
Fault diagnosis of anomalies in induction motors is essential to ensure industry safety. This paper presents a new hybrid Invasive Weed Optimization and Machine Learning approach for fault diagnosis in an induction motor. The vibration signal provides a lot of information about the motor's operating conditions. Therefore, the vibration signal of the motor was chosen to investigate the fault diagnosis. Two identical 400-V, 50-Hz, 4-pole 0.75 HP induction motors were under healthy, mechanical, and electrical faults tested in a laboratory with different loading. A hybrid model was developed using the vibration signal, the Invasive Weed Optimization algorithm (IWO), and machine learning classifiers. Some statistical features were extracted from the signal using Discrete Wavelet Transform (DWT). The invasive weed optimization algorithm (IWO) was utilized to reduce the number of the extracted features and select the most suitable ones. Then, three classification algorithms namely k-Nearest Neighbor neural network (KNN), Support Vector Machine (SVM), and Random Forest (RF), were trained using k-fold cross-validation and tested to predict the true class. The advantage of combining these techniques is to reduce the training time and increase the average accuracy of the model. The performance of the proposed fault diagnosis model was evaluated by measuring the Specificity, Accuracy, Precision, Recall, and F1_score. The experimental results prove that the proposed model has achieved more than 99.90% of accuracy. Furthermore, the other evaluation parameters also show the same representation of performance. The hybrid model has proved successfully its robust for diagnosing the faults under different load conditions.
