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The online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose induction motor faults. This work presents a reliable method for the detection of stator winding faults (which make up 38%...
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... These strategies can be classified into linear or non-linear strategies. Other strategies are no less important than the previously mentioned strategies, which are artificial intelligence, where fuzzy logic (Azgomi and Poshtan, 2013), neural networks (Yatsiuk and Husach, 2020), particle swarm optimization , and genetic algorithms (Ismail, 2012) were used to command the operation of the motor, especially the IM drive. These intelligent strategies have proven to dramatically improve machine characteristics. ...
... However, one of the crucial issues is the lack of a reliable mathematical model for describing a faulty motor. For instance, between-coil, short-circuit fault detection is performed by [4] utilizing phase current magnitudes and fuzzy logic; however, using the motor phase currents without comprehending their dynamics will require a performance of several simulations, either through a computer-based model as a finite element, or a considerable number of experiments to retrieve enough information that can be useful for implementing the fuzzy logic system in charge of detecting the fault. In [5], a methodology based on a fuzzy logic classifier is introduced for detecting stator faults in induction motors; however, since phase current magnitudes change depending on the motor load, an extensive database is required for the fuzzy logic system to be capable of detecting the fault. ...
... The number of short-circuited coils is equivalent to 12% of the winding. The fluctuations in the phase current amplitudes can be determined by (4). Figure 11 shows a higher escalation of the current I c amplitude since more coils are short-circuited by activating the tap T 3 in the start connection. ...
... In Figure 12, it can be observed once more that the amplitude of line current Iba remains without change, which can be deduced analytically by (4), whereas the magnitude of line currents Iac and Icb have more amplitude variations. Figure 11. ...
Online monitoring of induction motors has increased significantly in recent years because these devices are essential components of any industrial process. Incipient fault detection in induction motors avoids interruptions in manufacturing processes and facilitates maintenance tasks to reduce induction motor timeout. Therefore, the proposal of novel approaches to assist in the detection and classification of induction motor faults is in order. In this work, a reliable and noninvasive novel technique that does not require computational demanding operations, since it just performs arithmetic calculations, is introduced for detecting and locating short-circuit faults in the stator windings of an induction motor. This method relies on phasor analysis and the RMS values of line currents, followed by a small set of simple if-then rules to perform the diagnosis and identification of stator winding faults. Obtained results from different experimental tests on a rewound induction motor stator to induce short-circuit faults demonstrate that the proposed approach is capable of identifying and locating incipient and advanced deficiencies in the windings’ insulation with high effectiveness.
... As for the other two methods, moving average [10] and fuzzy logic [11], they are already considered traditional fault diagnosis techniques, along with k-nearest neighbor (KNN) [12] and support vector machine (SVM) [13]. An example of a fuzzy logic-based vibration analysis technique can be found in the work of Mukane et al. [14], which proposed a LabVIEW-based implementation of a fuzzy logic system to identify machine defects by carrying out vibration analysis. ...
... The fact that this method has been used for finding faults in a cooling fan presents a contribution to this paper. The formula for MA has been shown in Eq. (11), where = represents the average of a set of values at a certain interval and n represents the total number of intervals: ...
Industrial cooling fans are responsible for maintaining stable temperatures for delicate components. Therefore, a cooling system failure can certainly lead to machine downtime. Fault Condition Monitoring (FCM) is a predictive maintenance method that can be applied to cooling fans for fault prediction. As the components of a cooling fan wear off, its vibration tends to vary. Thus, this paper uniquely elaborates on three intelligent vibration analysis techniques that are applicable in the FCM of cooling fans. In this research, 1) image encoding with convolutional neural network (CNN), 2) moving average, and 3) fuzzy logic techniques are designed, employed, and their potentials as FCM tools are compared. The vibration data is collected from an experimental test bench that consists of a fan, an accelerometer, and a microcontroller, among others. Once enough data is obtained, the three vibration analysis techniques are applied using Python and MATLAB. The results reported in this paper demonstrate the effectiveness of these intelligent vibration analysis techniques in the FCM of cooling fans and possibly other industrial equipment. The novelty of the research revolves around the fan fault classification techniques that are being compared. The image-encoding technique described in this paper has yet to be applied for fault classification. Additionally, while fuzzy logic and moving average are popular methods, this is the first time that they are being used for vibration analysis of cooling fans. Furthermore, this is also a novel comparative study of different vibration analysis techniques.
... Each node in this layer has a function such as [19]: ...
The goal of this work was to study the best technique for fault diagnosis in bearing induction motors. Degraded operating modes may occur during the life of the induction motors. One of the main causes of these failures is the defects of the bearings. To improve the operational safety of the drives, monitoring facilities can be placed to perform preventive maintenance. We present a classification of the vibration vector signal based on the vibration data obtained from the vector signal for four types of bearing defects (healthy, ball defect, inner ring and outer ring defect). The automatic diagnosis of these vectors is performed using artificial intelligence techniques that combine retro-propagation neural network algorithm and fuzzy inference system adaptive network of type Takagi-Sugeno. These techniques give accurate results that are confirmed by numerical simulation.
... Feature extraction is a crucial step that transforms input data into a set of features which can then be classified by an algorithm [4]. Wavelet transform (WT) is a prominent feature extraction method that has been deployed successfully in conjunction with several conventional classification algorithms, such as fuzzy logic, k-nearest neighbor (KNN), or support vector machine [5][6][7]. For example, Konar and Chattopadhyay [8] proposed a hybrid WT and SVM method for detecting possible defects in ball bearings of motors. ...
In industries, cooling fans are vital in a wide range of machines to ensure a tolerable temperature for their intricate electronic components. Therefore, to avoid machine failure, a fault condition monitoring (FCM) system for cooling fans can be highly valuable. One way to monitor defects in rotational equipment is to analyze
the machine vibration, which varies as the components wear off. Hence, this paper presents a technique to diagnose faults in cooling fans by analyzing the vibration data. In this conference paper, convolutional neural networks (CNNs) are used to
classify the faults based on the vibration. The vibration data are collected from an experimental test bench that consists of a fan, an accelerometer, and a microcontroller, among others. The data were used to train the VGG16 and ResNet50 CNN architectures. The accuracy and effectiveness of these two architectures for vibration
analysis are compared in this paper.
... As for the other two methods, moving average [10] and fuzzy logic [11], they are already considered traditional fault diagnosis techniques, along with k-nearest neighbor (KNN) [12] and support vector machine (SVM) [13]. An example of a fuzzy logic-based vibration analysis technique can be found in the work of Mukane et al. [14], which proposed a LabVIEW-based implementation of a fuzzy logic system to identify machine defects by carrying out vibration analysis. ...
... The fact that this method has been used for finding faults in a cooling fan presents a contribution to this paper. The formula for MA has been shown in Eq. (11), where = represents the average of a set of values at a certain interval and n represents the total number of intervals: ...
Industrial cooling fans are responsible for maintaining stable temperatures for delicate components. Therefore, a cooling system failure can certainly lead to machine downtime. Fault Condition Monitoring (FCM) is a predictive maintenance method that can be applied to cooling fans for fault prediction. As the components of a cooling fan wear off, its vibration pattern tends to alter or become more erratic. Thus, this paper uniquely elaborates on three intelligent vibration analysis techniques that are applicable in the FCM of cooling fans. In this research, 1) image encoding with convolutional neural network (CNN), 2) moving average, and 3) fuzzy logic techniques are designed, employed, and their potentials as FCM tools are compared. The vibration data is collected from an experimental test bench that consists of a fan, an accelerometer, and a microcontroller, among others. Once sufficient training data is obtained (11000 data points for each of the fan's conditions), the three vibration analysis models are trained on that data using Python and MATLAB. The results reported in this paper illustrate the accuracy of these intelligent vibration analysis techniques in detecting faults in cooling fans. The novelty of the research revolves around the fan fault detection techniques that are being compared. The image-encoding technique described in this paper has yet to be applied for fault classification and detection. Additionally, while fuzzy logic and moving average are popular methods, this is the first time that they are being used for vibration analysis of cooling fans. Furthermore, this is also a novel comparative study of different vibration analysis techniques.
... Mostly the examinations were performed on frequency domain in these studies [12][13][14][15]. Some studies used the fuzzy logic [16,17] and ANNs to determine the presence of failure [18][19][20][21][22][23][24]. Also, artificial neural networks were used for determining the presence and type of failures in the internal mechanism of motor [25]. ...
In this study, a new measurement system was developed to determine failures and to define the level of failure that may occur in bearings and rotor bearings or in foot of motor in single phase capacitor start motor. In the system, the vibratory operation of the motor is provided by connecting different screws on the motor’s rotor mounted flywheel or by gradually removing the nut bolts of motor foot. The VB3 vibration sensor outputs were recorded to the computer with LabVIEW program at 1 ms intervals for one minute. The changing characteristics of sensor output for each experiment had more than one frequency component; therefore, Fast Fourier Transform (FFT) was performed for determining such components. When the obtained FFT graphs were analyzed, it was determined that the vibrations had harmonics of 50 Hz and its multiples; and it was observed that the frequency and amplitude values of first 5 harmonics could be used for determining the presence, type and level of failure but there was a nonlinear relation between each other. Therefore, 2 different artificial neural networks (ANN) customized separately were developed for determining the type and rate of the failure of motor. 80%, 10% and 10% of available data were reserved for training, testing and verification, respectively, and the ANN was trained. Accuracy degree for the ANN in the estimations following the training stage was calculated as R = 0.97–0.98. Furthermore, the results of ANN were compared with the results obtained using Sequential Minimal Optimization, Naive Bayes (NB) and J48 algorithms; and it was determined that the accuracy degree of ANN was higher. After this, a program was developed in MATLAB in order to work 2 ANNs with highest success together. Lastly, a system consisting of Raspberry Pi and a 7″ LCD screen, similar to the multimedia system in cars, was created to use at industrial applications.
... The improvement of the latest strategies is permanently required to support to clear up problems [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . ...
In the new era of technology, there is the redundancy of information in the internet world, which gives a hard time for users to contain the willed outcome it, to crack this hardship we need an automated process that riddle and search the obtained facts. Text summarization is one of the normal methods to solve problems. The target of the single document epitome is to raise the possibilities of data. we have worked mostly on extractive stationed text summarization. Sentence scoring is the method usually used for extractive text summarization. In this paper, we built an Urdu Roman Language Dataset which has thirty thousand articles. We follow the Fuzzy good judgment technique to clear up the hassle of text summarization. The fuzzy logic approach model delivers Fuzzy rules which have uncertain property weight and produce an acceptable outline. Our approach is to use Cosine similarity with Fuzzy logic to suppress the extra data from the summary to boost the proposed work. We used the standard Testing Method for Fuzzy Logic Urdu Roman Text Summarization and then compared our Machine-generated summary with the help of ROUGE and BLEU Score Method. The result shows that the Fuzzy Logic approach is better than the preceding avenue by a meaningful edge.
... Taking advantage of the real-time simulation model, we present our approach in designing the control algorithm and closing the loop in a systematic way. We employ fuzzy logic, as a knowledge-based control approach, to control simulated cognitive stress state [56], [57]. The knowledge-based control approaches make inference and design the control action using the insight derived from system dynamics. ...
... On the other hand, when we deal with the low arousal stress state, we need excitation control to increase the number of SCR events and elevate the stress-related state. Similar to [1], we use Mamdani engine and centroid method for inference and defuzzification, respectively [57], [59]. ...
Keeping cognitive stress at a healthy range can improve the overall quality of life: helping subjects to decrease their high levels of arousal, which will make them relaxed, and elevate their low levels of arousal, which could increase their engagement. With recent advances in wearable technologies, collected skin conductance data provides us with valuable information regarding ones’ cognitive stress-related state. In this research, we aim to create a simulation environment to control a cognitive stress-related state in a closed-loop manner. Toward this goal, by analyzing the collected skin conductance data from different subjects, we model skin conductance response events as a function of simulated environmental stimuli associated with cognitive stress and relaxation. Then, we estimate the hidden stress-related state by employing Bayesian filtering. Finally, we design a fuzzy control structure to close the loop in the simulation environment. Particularly, we design two classes of controllers: (1) an inhibitory controller for reducing cognitive stress and (2) an excitatory controller for increasing cognitive stress. We extend our previous work by implementing the proposed approach on multiple subjects’ profiles. Final results confirm that our simulated skin conductance responses are in agreement with experimental data. In a simulation study based on experimental data, we illustrate the feasibility of designing both excitatory and inhibitory closed-loop wearable-machine interface architectures to regulate the estimated cognitive stress state. Due to the increased ubiquity of wearable devices capable of measuring cognitive stress-related variables, the proposed architecture is an initial step to treating cognitive disorders using non-invasive brain state decoding.
... The Mamdani fuzzy logic approach is presented in [26,[28][29][30][31]. The first step is fuzzification. ...
... The decision-maker procedure based on "IF symptom is … THEN fault is …" algorithm is shown in Fig.11. The defuzzification procedure could be a yes-no decision on the appearance of faults and the decision for the action (for example, stopped the device) as shown in [28]. In [29,30], the crisp output found the central method and meaning motor conditional in considering faults in motor and devices electrical circuits. ...
Any energy conversion devices, such as industrial motor-drives, propulsion drives of electric vehicles, pump systems, wind turbines, and others, are prone to failures. Usually, failures result in increased economic costs that come through additional energy losses, loss of production, or in a worst-case even environmental hazard. To prevent failures, energy conversion systems may be checked through particular routines developed and specified by the manufactures. However, it may be challenging due to the complex construction of energy conversion devices or devices' failure between the routine checks. Such schedule-based condition monitoring approaches provide minor information on the remaining lifetime (separate components and whole system) of the devices and do not allow proper prognostic or full exploitation. To overcome traditional two-level Boolean approaches with healthy/faulty states an Artificial Intelligence (AI)-based control techniques are used. The Fuzzy Logic approach is based on inspired by human perception processes and cognition that are often uncertain or empirical. However, Fuzzy Logic is already successfully applied in various control applications of energy conversion devices, even when the analytical models are unknown. This paper argues for developing new fault detection algorithms based on fuzzy logic methods to allow energy conversion systems designers to develop reliability factors for apparatus, which included electrical machines and power electronics subsystems.
