scholarly journals Increasing Electric Vehicles Reliability by Non-Invasive Diagnosis of Motor Winding Faults

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2510
Author(s):  
Konrad Górny ◽  
Piotr Kuwałek ◽  
Wojciech Pietrowski
Keyword(s):  
Electric Vehicles ◽  
Diagnostic Method ◽  
Induction Motors ◽  
Early Stage ◽  
Short Circuit ◽  
Damage Classification ◽  
Non Invasive ◽  
Signal Features ◽  
Empirical Wavelet Transform ◽  
Simulation Results

The article proposes a proprietary approach to the diagnosis of induction motors allowing increasing the reliability of electric vehicles. This approach makes it possible to detect damage in the form of an inter-turn short-circuit at an early stage of its occurrence. The authors of the article describe an effective diagnostic method using the extraction of diagnostic signal features using an Enhanced Empirical Wavelet Transform and an algorithm based on the method of Ensemble Bagged Trees. The article describes in detail the methodology of the carried out research, presents the method of extracting features from the diagnostic signal and describes the conclusions resulting from the research. Phase current waveforms obtained from a real object as well as simulation results based on the field-circuit model of an induction motor were used as a diagnostic signal in the research. In order to determine the accuracy of the damage classification, simple metrics such as accuracy, sensitivity, selectivity, precision as well as complex metrics weight F1 and macro F1 were used.

scholarly journals Identification of Inter-Turn Short-Circuits in Induction Motor Stator Winding Using Simulated Annealing

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 117
Author(s):  
Marcin Tomczyk ◽  
Ryszard Mielnik ◽  
Anna Plichta ◽  
Iwona Goldasz ◽  
Maciej Sułowicz
Keyword(s):  
Simulated Annealing ◽  
Induction Motor ◽  
Induction Motors ◽  
Early Stage ◽  
Dynamic Properties ◽  
Short Circuit ◽  
State Variables ◽  
Novel Approach ◽  
Short Circuits ◽  
Identification Processes

This paper presents a method of inter-turn short-circuit identification in induction motors during load current variations based on a hybrid analytic approach that combines the genetic algorithm and simulated annealing. With this approach, the essence of the method relies on determining the reference matrices and calculating the distance between the reference matric values and the test matrix. As a whole, it is a novel approach to the process of identifying faults in induction motors. Moreover, applying a discrete optimization algorithm to search for alternative solutions makes it possible to obtain the true minimal values of the matrices in the identification process. The effectiveness of the applied method in the monitoring and identification processes of the inter-turn short-circuit in the early stage of its creation was confirmed in tests carried out for several significant state variables describing physical magnitudes of the selected induction motor model. The need for identification of a particular fault is related to a gradual increase in its magnitude in the process of the induction motor’s exploitation. The occurrence of short-circuits complicates the dynamic properties of the measured diagnostic signals of the system to a great extent.

scholarly journals Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8523
Author(s):  
Marcin Tomczyk ◽  
Ryszard Mielnik ◽  
Anna Plichta ◽  
Iwona Gołdasz ◽  
Maciej Sułowicz
Keyword(s):  
Genetic Algorithm ◽  
Induction Motor ◽  
Diagnostic Method ◽  
Induction Motors ◽  
Short Circuit ◽  
Experimental Tests ◽  
Stator Winding ◽  
Short Circuits ◽  
Cage Induction Motor ◽  
Industry Conditions

This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.

Fault Diagnosis of Induction Motors Based on RBF Neural Network

2013 ◽  
Vol 462-463 ◽  
pp. 85-88 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Rbf Neural Network ◽  
Short Circuit ◽  
Three Phase ◽  
Diagnosis Accuracy ◽  
Simulation Results

In order to improve the diagnosis accuracy of stator short circuit faults of three-phase induction motors, in this paper, a method using three-layered RBF neural network is proposed to diagnose the short circuit faults on the basis of analysis of structure and algorithm of RBF neural network. Then the approach to establish RBF neural network and the influence of different expanding coefficients upon the diagnosis accuracy are illustrated. The simulation results show that RBF neural network can successfully diagnose and classify six typical short circuit faults of induction motors. This method has a faster speed, higher accuracy and it needs fewer samples. In conclusion, RBF neural network is practical, efficient and intelligent in fault diagnosis of induction motors.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

scholarly journals Analysis of Torque Ripples of an Induction Motor Taking into Account a Inter-Turn Short-Circuit in a Stator Winding

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3626 ◽  
Author(s):  
Wojciech Pietrowski ◽  
Konrad Górny
Keyword(s):  
Early Stage ◽  
Short Circuit ◽  
Torque Ripple ◽  
Drive System ◽  
Electrical Machines ◽  
Stator Winding ◽  
Permanent Magnet Synchronous Machines ◽  
Noninvasive Diagnostics ◽  
Drive Systems ◽  
Commercial Applications

Despite the increasing popularity of permanent magnet synchronous machines, induction motors (IM) are still the most frequently used electrical machines in commercial applications. Ensuring a failure-free operation of IM motivates research aimed at the development of effective methods of monitoring and diagnostic of electrical machines. The presented paper deals with diagnostics of an IM with failure of an inter-turn short-circuit in a stator winding. As this type of failure commonly does not lead immediately to exclusion of a drive system, an early stage diagnosis of inter-turn short-circuit enables preventive maintenance and reduce the costs of a whole drive system failure. In the proposed approach, the early diagnostics of IM with the inter-turn short-circuit is based on the analysis of an electromagnetic torque waveform. The research is based on an elaborated numerical field–circuit model of IM. In the presented model, the inter-turn short-circuit in the selected winding has been accounted for. As the short-circuit between the turns can occur in different locations in coils of winding, computations were carried out for various quantity of shorted turns in the winding. The performed analysis of impact of inter-turn short-circuit on torque waveforms allowed to find the correlation between the quantity of shorted turns and torque ripple level. This correlation can be used as input into the first layer of an artificial neural network in early and noninvasive diagnostics of drive systems.

scholarly journals Optimal Decision-Making to Charge Electric Vehicles in Heterogeneous Networks: Stackelberg Game Approach

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 325 ◽  
Author(s):  
Shijun Chen ◽  
Huwei Chen ◽  
Shanhe Jiang
Keyword(s):  
Electric Vehicles ◽  
Heterogeneous Networks ◽  
Demand Uncertainty ◽  
Stackelberg Game ◽  
Optimal Solution ◽  
Stackelberg Equilibrium ◽  
Optimal Decision ◽  
Charging Station ◽  
Simulation Results ◽  
Grid Operators

Electric vehicles (EVs) are designed to improve the efficiency of energy and prevent the environment from being polluted, when they are widely and reasonably used in the transport system. However, due to the feature of EV’s batteries, the charging problem plays an important role in the application of EVs. Fortunately, with the help of advanced technologies, charging stations powered by smart grid operators (SGOs) can easily and conveniently solve the problems and supply charging service to EV users. In this paper, we consider that EVs will be charged by charging station operators (CSOs) in heterogeneous networks (Hetnet), through which they can exchange the information with each other. Considering the trading relationship among EV users, CSOs, and SGOs, we design their own utility functions in Hetnet, where the demand uncertainty is taken into account. In order to maximize the profits, we formulate this charging problem as a four-stage Stackelberg game, through which the optimal strategy is studied and analyzed. In the Stackelberg game model, we theoretically prove and discuss the existence and uniqueness of the Stackelberg equilibrium (SE). Using the proposed iterative algorithm, the optimal solution can be obtained in the optimization problem. The performance of the strategy is shown in the simulation results. It is shown that the simulation results confirm the efficiency of the model in Hetnet.

scholarly journals Panel of serum miRNAs as potential non-invasive biomarkers for pancreatic ductal adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Imteyaz Ahmad Khan ◽  
Safoora Rashid ◽  
Nidhi Singh ◽  
Sumaira Rashid ◽  
Vishwajeet Singh ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Early Stage ◽  
Ductal Adenocarcinoma ◽  
Next Generation ◽  
Serum Samples ◽  
Tissue Samples ◽  
Non Invasive ◽  
Specific Symptoms ◽  
Generation Sequencing

AbstractEarly-stage diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to non-specific symptoms. Circulating miRNAs in body fluids have been emerging as potential non-invasive biomarkers for diagnosis of many cancers. Thus, this study aimed to assess a panel of miRNAs for their ability to differentiate PDAC from chronic pancreatitis (CP), a benign inflammatory condition of the pancreas. Next-generation sequencing was performed to identify miRNAs present in 60 FFPE tissue samples (27 PDAC, 23 CP and 10 normal pancreatic tissues). Four up-regulated miRNAs (miR-215-5p, miR-122-5p, miR-192-5p, and miR-181a-2-3p) and four down-regulated miRNAs (miR-30b-5p, miR-216b-5p, miR-320b, and miR-214-5p) in PDAC compared to CP were selected based on next-generation sequencing results. The levels of these 8 differentially expressed miRNAs were measured by qRT-PCR in 125 serum samples (50 PDAC, 50 CP, and 25 healthy controls (HC)). The results showed significant upregulation of miR-215-5p, miR-122-5p, and miR-192-5p in PDAC serum samples. In contrast, levels of miR-30b-5p and miR-320b were significantly lower in PDAC as compared to CP and HC. ROC analysis showed that these 5 miRNAs can distinguish PDAC from both CP and HC. Hence, this panel can serve as a non-invasive biomarker for the early detection of PDAC.

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