Search Results (85)

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

Thermal control in rolling mills motors is gaining importance as more and more hard-to-deform steel grades are rolled. The capabilities of diagnostics monitoring also expand as digital IIoT-based technologies are adopted. Electrical drives in modern rolling mills are based on synchronous motors with frequency regulation. Such motors are expensive, while their reliability impacts the metallurgical plant output. Hence, developing the on-line temperature monitoring systems for such motors is extremely urgent. This paper presents a solution applying to synchronous motors of the upper and lower rolls in the horizontal roll stand of plate mill 5000. The installed capacity of each motor is 12 MW. According to the digitalization tendency, on-line monitoring systems should be based on digital shadows (coordinate observers) that are similar to digital twins, widely introduced at metallurgical plants. Modern reliability requirements set the continuous temperature monitoring for stator and rotor windings and iron core. This article is the first to describe a method for calculating thermal loads based on the data sets created during rolling. The authors have developed a thermal state observer based on four-mass model of motor heating built using the Simscape Thermal Models library domains that is part of the MATLAB Simulink. Virtual adjustment of the observer and of the thermal model was performed using hardware-in-the-loop (HIL) simulation. The authors have validated the results by comparing the observer’s values with the actual values measured at control points. The discrete masses heating was studied during the rolling cycle. The stator and rotor winding temperature was analysed at different periods. The authors have concluded that the motors of the upper and lower rolls are in a satisfactory condition. The results of the study conducted generally develop the idea of using object-oriented digital shadows for the industrial electrical equipment. The authors have introduced technologies that improve the reliability of the rolling mills electrical drives which accounts for the innovative development in metallurgy. The authors have also provided recommendations on expanded industrial applications of the research results. Full article

Traditional motor temperature rise testing generally uses temperature sensors. To solve problems such as sensor detachment, aging, and space occupation, this study takes a three-phase asynchronous motor as an example to propose a method for building a temperature rise monitoring model driven by a multi-physics field model based on the digital twin framework of power equipment. A twin monitoring model with defined input–output parameters is constructed to solve the problems of measurement inconvenience in traditional methods. Firstly, the losses of the iron core and the winding copper in the motor were obtained through electromagnetic field simulation. Secondly, the temperature distribution of the motor stator was obtained based on the bidirectional coupling characteristics of the magnetic and thermal fields. Subsequently, a temperature field reduced-order model based on the proper orthogonal decomposition method was built in Twin Builder, achieving fast calculation of the motor stator temperature. Finally, using the YE3-80M1-4 motor as the experimental subject, the model’s output results were compared with and validated against the experimental results. The results indicate that the simulation time of the reduced-order model is 2.1 s, and the relative error compared with the test values is within 5%, which confirms the practical applicability of the proposed method. Full article

Electric vehicle applications frequently use brushless direct-current (BLDC) motors due to their high torque and efficiency. However, coil damage may result from their use at high rotating speeds and extremely high temperatures, requiring preventative maintenance. This study describes the creation of a better online monitoring platform that is coupled with an improved fault detection and protection system for small electric vehicles. Designing a fault detection system with real-time analysis to identify open-circuit problems is part of the process. The results indicate that the reliability and operating efficiency of electric vehicle applications have been greatly enhanced by the development of a potential fault-monitoring and protection solution. Full article

This review explores sensor selection and placement strategies for electric motor monitoring in industrial settings. A wide range of sensor types including temperature, vibration, current, and position sensors—are evaluated in terms of their technical features and application constraints. Preliminary experimental data on vibration sensors highlight how signal amplitude varies with sensor placement, reinforcing the importance of correct positioning. However, this study stops short of applying AI/ML techniques to optimize placement. Accordingly, this paper serves as a foundational step toward developing intelligent sensor deployment frameworks. Future work will build on this review by integrating supervised learning, dimensionality reduction, and reinforcement learning techniques to automate sensor placement and improve condition monitoring in electric motors. Full article

We present a detailed dataset collected via a wireless IoT sensor network monitoring three industrial centrifugal pumps (units A, B, and C) at the Italian Aerospace Research Centre (CIRA), along with the methods for data collection and structuring. Background: Centrifugal pumps are critical in industrial plants, and monitoring their condition is essential to ensure reliability, safety, and efficiency. High-quality operational data under normal operating conditions are fundamental for developing effective maintenance strategies and diagnostic models. Methods: Data were gathered by means of smart sensors measuring motor and pump vibrations, temperatures, outlet fluid pressures, and environmental conditions. Data were transmitted over a WirelessHART mesh network and acquired through an IoT architecture. Results: The dataset consists of eight CSV files, each representing a specific pump during a distinct operational day. Each file includes timestamped measurements of displacement, peak vibration values, sensor temperatures, fluid pressure, ambient temperature, and atmospheric pressure. Conclusions: This dataset supports advanced methodologies in feature extraction, multivariate signal analysis, unsupervised pattern discovery, vibration analysis, and the development of digital twins and soft sensing models for predictive maintenance optimization. Full article

The aim of this study was to evaluate machine learning algorithms’ capacity to improve prescriptive maintenance. A pumping system consisting of two hydraulic pumps with an electric motor from a Spanish petrochemical company was used as a case study. Sensors were used to record data on the variables, with the target variable being the bearing temperature of the electric motor. Several regression models and a neural network time series model were tested to model the system variables. A bearing temperature sensitivity analysis was conducted based on the coefficients obtained from the optimization of the regression model. To fully exploit the capabilities of these techniques for application in this field, we designed a reference framework intended to foster model deployment in an industrial context by promoting the self-monitoring and updating of the models when required. The impact on decision-making processes is explored using reinforcement learning in the context of this framework. Full article

This paper presents a Virtualized Computational Radio Frequency Identification (VCRFID) solution that utilizes far-field UHF RF for sensing, computing, and self-powering at the edge. A standard UHF RFID system is asymmetric as it consists of a relatively large, complex “reader”, which acts as an RF transmitter and controller for a number of small simple battery-less “tags”, which work in passive mode as they communicate and harvest RF energy from the reader. Previously proposed Computational RFID (CRFID) solutions enhance the standard RFID tags with microcontrollers and sensors in order to gain enhanced functionality, but they end up requiring a relatively high level of power, and thus ultimately reduced range, which limits their use for many Internet-of-Things (IoT) application scenarios. Our VCRFID solution instead keeps the functionality of the tags minimalistic by only providing a sensor interface to be able to capture desired environmental data (temperature, humidity, vibration, etc.), and then transmit it to the RFID reader, which then performs all the computational load usually carried out by a microcontroller on the tag in prior work. This virtualization of functions enables the design of a circuit without a microcontroller, providing greater flexibility and allowing for wireless reconfiguration of tag functions over RF for a 97% reduction in energy consumption compared to prior energy-harvesting RFID tags with microcontrollers. The target application is Industry 4.0 where our VCRFID solution enables battery-less fine-grain monitoring of vibration and temperature data for pumps and motors for predictive maintenance scenarios. Full article

This study presents a comparative analysis of various machine learning algorithms to evaluate their performance in diagnosing faults within automated manufacturing systems. The primary objective is to identify the most effective model for classifying equipment failures based on historical data. Several algorithms were selected, including support vector machines (SVM), Decision trees, boosting, random forest, k-nearest neighbors (KNN), stacking, and artificial neural networks. The research began with the collection of a dataset using an Arduino-based system with sensors (temperature, electrical current, differential pressure, vibration, and sound) to monitor the equipment’s operational condition. Faults were intentionally induced in a motor, an electrovalve, and a pneumatic cylinder. The data were then processed in a Python environment, undergoing normalization and dimensionality reduction. The models were evaluated through cross-validation and compared using metrics such as precision, recall, F1-score, and accuracy. Results indicated that all models performed well, with the SVM algorithm showing the best overall performance, with an average fault diagnosis accuracy of 91.62% when trained on the full dataset and 66.83% under extreme class imbalance. In contrast, decision trees demonstrated lower generalization ability. This study provides insights for future fault diagnosis research using machine learning and offers recommendations for implementing such technologies in industrial environments. Full article

The thermal error of the high-power grinding motorized spindle, caused by heating, seriously affects machining accuracy. In this paper, an ensemble learning algorithm is used to predict the thermal error of a high-precision motorized spindle. The subsequent problem of thermal error compensation can be effectively solved by a suitable thermal error model, which is crucial for improving the machining accuracy of the actual machining process. Firstly, the steady-state temperature field of the grinding motorized spindle is analyzed and used to determine the position of the sensors. Then, a signal acquisition instrument is used to monitor real-time temperature data. After that, experimental results are obtained, followed by verification. Finally, based on experimental data and the optimization results of temperature measurement points, temperature data are used as the input variable, and thermal deformation data are used as the output variable. The ensemble learning model is composed of different weak learners, which include multiple linear regression, back-propagation, and radial basis function neural networks. Different weak learners are trained using datasets separately, and the output of the weak learners is used as input to the model. Through integrating strategies, an ensemble learning model is established and compared with a weak learner. The error residual set of the ensemble learning model remains within [−0.2, 0.2], and the prediction performance shows that the ensemble learning model has a better predictive effect and strong robustness. Full article

The temperature variations in wind turbine motors and gearboxes are closely related to their health status, making accurate temperature prediction essential for operational monitoring and early fault detection. However, conventional deep learning-based temperature prediction methods, such as recurrent neural networks (RNN) and convolutional neural networks (CNN) and their hybrid models, often face challenges in capturing both local feature dependencies and long-term temporal patterns in complex, nonlinear temperature fluctuations. To address these limitations, this paper proposes a hybrid model based on depthwise separable convolutional neural networks (DSCNNs) and bidirectional long short-term memory (BiLSTM) networks. The DSCNN module enhances feature extraction from temperature signals, while the BiLSTM module captures long-term dependencies, improving prediction accuracy and robustness. Experimental validation using temperature data from a wind farm in Shaanxi, China, demonstrates that the proposed model outperforms existing deep learning approaches, achieving superior prediction accuracy, better adaptability to temperature fluctuations, and greater robustness in handling complex nonlinear dynamics. Furthermore, the proposed model provides an effective solution for early fault detection in wind turbines, including both mechanical faults (e.g., gearbox wear, bearing overheating) and electrical faults (e.g., winding short circuits, overload conditions), contributing to more reliable condition monitoring in industrial applications. Full article

With the growing global focus on environmental protection and carbon emissions, electric vehicles (EVs) are becoming increasingly popular. Permanent magnet synchronous motors (PMSMs) have emerged as a core component of the drive system due to their high-power density and compact design. The rotor temperature of PMSMs significantly affects their operating efficiency, management strategies, and lifespan. However, real-time monitoring and acquisition of rotor temperature are challenging due to cost and space limitations. Therefore, this study proposes a hybrid model named RIME-XGBoost, which integrates the RIME optimization algorithm with XGBoost, for the precise modeling and prediction of PMSM rotor temperature. RIME-XGBoost utilizes easily monitored dynamic parameters such as motor speed, torque, and currents and voltages in the d-q coordinate system as input features. It simultaneously optimizes three hyperparameters (number of trees, tree depth, and learning rate) to achieve high learning efficiency and good generalization performance. The experimental results show that, on both medium-scale datasets and small-sample datasets in high-temperature ranges, RIME-XGBoost outperforms existing methods such as SMA-RF, SO-BiGRU, and EO-SVR in terms of RMSE, MBE, R-squared, and Runtime. RIME-XGBoost effectively enhances the prediction accuracy and computational efficiency of rotor temperature. This study provides a new technical solution for temperature management in EVs and offers valuable insights for research in related fields. Full article

The accurate prediction of temperature in Permanent Magnet Synchronous Motors (PMSMs) has always been essential for monitoring performance and enabling predictive maintenance in the industrial sector. This study examines the efficiency of a set of artificial neural network (ANN) models, namely Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), in predicting the Permanent Magnet Temperature. A comparative evaluation study is conducted using common performance indicators, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), to assess the predictive accuracy of each model. The intent is to identify the most favorable model that balances high accuracy with low computational cost. Full article

Silicon carbide (SiC) metal oxide semiconductor field-effect transistors (MOSFETs) are a future trend in traction inverters in electric vehicles (EVs), and their thermal safety is crucial. Temperature-sensitive electrical parameters’ (TSEPs) indirect detection normally requires additional circuits, which can interfere with the system and increase costs, thereby limiting applications. Therefore, there is still a lack of cost-effective and sensorless thermal monitoring techniques. This paper proposes a high-efficiency datasheet-driven method for sensorless estimation utilizing the third-quadrant characteristics of MOSFETs. Without changing the existing hardware, the closure degree of MOS channels is controlled through a dual-gate bias (DGB) strategy to achieve reverse conduction in different patterns with body diodes. This method introduces a MOSFET operating current that TSEPs are equally sensitive to into the two-argument function, improving the complexity and accuracy. A two-stage current pulse is used to decouple the motor effect in various conduction modes, and the TSEP-combined temperature function is built dynamically by substituting the currents. Then, the junction temperature is estimated by the measured bus voltage and current. Its effectiveness was verified through spice model simulation and a test bench with a three-phase inverter. The average relative estimation error of the proposed method is below $7.2\%$ in centigrade. Full article

The coastal waters of Zhejiang feature a complex aquatic environment and abundant biological resources, creating an ideal habitat for various fish species. However, the systematic monitoring of fish larvae in these offshore waters is limited. This study collected 24,232 fish larvae using large plankton nets during April and November 2022, as well as February and July 2023, and identified 93 species, primarily warm-temperate and warm-water species, with a peak occurrence in summer. The dominant species include Larimichthys croceus, Sebastiscus marmoratus, Lateolabrax japonicus, and Odontamblyopus lacepedii, among others, and these species exhibit frequent seasonal changes. Fish larvae are typically found to be aggregated along estuaries and bays in spring, autumn, and summer, while in winter, they tend to shift towards areas near the boundaries of motor trawler fisheries areas. Our cluster analysis revealed spatial heterogeneity in the community structure, driven by an abundance of dominant and important species. Our Mantel tests and canonical correspondence analysis (CCA) identified seawater temperature and salinity as core drivers of the aggregation and distribution of fish larvae, interacting with factors such as the chlorophyll-a concentration, water turbidity, water depth, and dissolved oxygen. This research provides a scientific basis for the dynamic monitoring of spawning grounds and effective management of fishery resources in Zhejiang’s coastal waters. Full article