Adaptív wavelet-alapú zajcsökkentő eljárás mobilrobot távérzékelési adatfeldolgozó rendszerében

A mobilrobotok távérzékeléséből nyert adatainak zajszűrése a kívánt pontosságú digitális környezetmodell előállításához napjainkban is kihívást jelent. A távérzékelés során előállított pontfelhő számos forrásból eredő torzításokat tartalmazhat. Jelen munkában a mobilrobotok távérzékelési rendszerében az adatok előfeldogozására alkalmas wavelet-alapú eljárást mutatunk be. A wavelet transzformáció segítségével alacsony felbontási szinten is jól elkülöníthető a zaj a jel fontos részleteit tartalmazó részeitől. Ezekre alkalmazva a robusztus illesztést az extrém értékek, torzítások is eltávolíthatóak, szemben a klasszikus wavelet alapú szűrő eljárásokkal

intelligent neural network design for nonlinear control using simultaneous perturbation stochastic approximation spsa optimization

Simultaneous Perturbation Stochastic Approximation (SPSA) Optimization Adrienn Dineva*, Annamaria R. Varkonyi-Koczy**, Jozsef K. Tar*** and Vincenzo Piuri**** *Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, Budapest, Hungary *Doctoral School of Computer Science, Universita' degli Studi di Milano, Crema, Italy **Institute of Mechatronics & Vehicle Engineering, Obuda University, Budapest Hungary ** Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia ***Institute of Applied Mathematics, Obuda University, Budapest, Hungary **** Department of Computer Science, Universita' degli Studi di Milano, Crema, Italy E-mail: * [email protected], ** [email protected], ***[email protected], ****[email protected]

Review of soft computing models in design and control of rotating electrical machines

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines

Data-Driven Onboard Inter-Turn Short Circuit Fault Diagnosis for Electric Vehicles by Using Real-Time Simulation Environment

Various fault detection methods, particularly focused on onboard Condition-Based Monitoring (CBM) in Electrical Machines and Drives (EMDs), face limitations such as sensitivity to load variations, slow fault detection, and the absence of fully automated solutions. AI and Data-Driven methods offer flexible alternatives, utilizing historical data for pattern and anomaly identification. Among Electrical Signature Analysis techniques for electrical motor diagnostics, the Space Vector Theory (SVT) is extensively used, while Park’s Vector based diagnostic solutions lack real-time Inter-Turn Short Circuit (ITSC) fault severity assessment, with available techniques often limited to binary classifiers. Implementing AI with SVT for real-time Electric Vehicle (EV) use is underdeveloped, hindered by data scarcity and diverse dataset collection challenges. Real-time simulation, accurate fault modeling, and hardware limitations pose challenges, especially for embedding AI models into processors. To achieve intelligent onboard diagnosis for ITSC fault severity in this paper, a multi-modal approach model is proposed, employing MobileNetV2 to classify Park’s Vector trajectories based on the fault features related to the number of shorted turns. Performance assessments encompass both the standard MobileNetV2 and the proposed multi-modal approach model across various fault severity levels. Furthermore, to address the challenge of limited data availability, an accelerated real-time AI development environment is designed using an FPGA to generate synthetic fault pattern datasets, aligning with the standards of the Electric Vehicle industry. For modeling PMSM with ITSC faults, a fault circuit model is employed. The dataset of 900 Park’s Vector trajectory images is automatically generated by varying the torque request from 10 to 100 Nm with a 10 Nm resolution. At each torque operating point, the motor currents are recorded by adjusting the number of shorted turns. Simulation results confirm the outstanding performance of MobileNetV2 in binary classification, achieving an accuracy of 99.26 %. In case of 5-class ITSC fault severity classification, the prediction accuracy reaches only 72.55 %. The here proposed multi-modal MobileNetV2 model excels, achieving a remarkable accuracy of 99.163 % in the 3-class fault severity classification and 84.907 % in the 5-class classification. These results support the superiority of the proposed multi-modal MobileNetV2 model, which is trained on the generated rich dataset. It outperforms existing Park’s Vector Analysis based ITSC fault detection methods, particularly in early ITSC fault detection as it can detect faults from 6 shorted turns. Additionally, it allows for online fault severity assessment during transient operation and meets stringent requirements for onboard applications. Altogether, the results of investigations prove the presence and extractability of fine detail information in Park’s Vector trajectories, for assessing ITSC fault severity. This contributes to a deeper understanding and analysis of faults in electrical motors through the use of Park’s Vector trajectories

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment

Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement

Prediction of the remaining service life (RSL) of pavement is a challenging task for road maintenance and transportation engineering. The prediction of the RSL estimates the time that a major repair or reconstruction becomes essential. The conventional approach to predict RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise operational safety. In this paper, surface distresses of pavement are used to estimate the RSL to address the aforementioned challenges. To implement the proposed theory, 105 flexible pavement segments are considered. For each pavement segment, the type, severity, and extent of surface damage and the pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include falling weight deflectometer (FWD) and ground-penetrating radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include support vector regression (SVR), support vector regression optimized by the fruit fly optimization algorithm (SVR-FOA), and gene expression programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE), scattered index (SI), and Willmott’s index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement

Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins

The mountainous watersheds are increasingly challenged with extreme erosions and devastating floods due to climate change and human interventions. Hazard mapping is essential for local policymaking for prevention, planning the mitigation actions, and also adaptation to extremes. This study proposes novel predictive models for susceptibility mapping for flood and erosion. Furthermore, this study elaborates on prioritizing the existing sub-basins in terms of erosion and flood susceptibility. A comparative analysis of generalized linear model (GLM), flexible discriminate analyses (FDA), multivariate adaptive regression spline (MARS), random forest (RF), and their ensemble is performed to ensure highest predictive performance. Furthermore, the priority of the sub-basins in terms of sensitivity to erosion and flood was determined based on the best model. The results showed that the GLM, FDA, MARS, RF, and ensemble models had an area under curve (AUC) 0.91, 0.92, 0.89, 0.93 and 0.94, respectively, in modeling the flood susceptibility. Also, the GLM, FDA, MARS, RF, and ensemble models had an AUC of 0.93, 0.92, 0.89, 0.96, and 0.97, respectively, in determining erosion susceptibility. Priority assessment based on the best model, the ensemble approach, indicated that the sub-basins SW3 and SW5 were found to have high sensitivity to the flood and soil erosion, respectively