In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies.

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.

In the era of rapidly advancing technological landscapes, the fusion of cutting-edge sensor technology, innovations in electric vehicles, and artificial intelligence are driving the growth of diagnosis and prognostics health management techniques tailored for electric vehicle applications. Such a system plays a pivotal role in ensuring the reliable, efficient, and safe operation of complex engineering systems like electric vehicles by facilitating fault detection, diagnosis, recovery, and prognostics. The main challenge in the field of diagnosis, prognostics and health management is to pinpoint the optimal methods and areas of application in electric vehicle to extract the most pertinent information. In this paper, a novel intelligent digital twin-based architecture for intelligent diagnosis, prognostics and health management of electric vehicle powertrains is proposed, it aims to address the aforementioned challenges. The proposed architecture is based on a multilayered approach that integrates multiple intelligent digital twin models at different abstraction levels and leverages transfer learning strategies to enable individualization and servitization of the proposed system. Furthermore, pivotal technologies enabling this approach are delved into, along with a discussion of inherent challenges and emerging opportunities. The findings underscore the benefit of using distributed computing and real-time prognostics and health management information to enhance the reliability, efficiency, and availability of the electric vehicles, concurrently ensuring driver safety. The proposal is enriched with a case study that illustrates the practical application of our architecture in a real-world electric vehicle monitoring system scenario, showcasing its potential for customization and service-oriented features, and provides foresight into vehicle parameter trends.

Digitalization in mining industry has become an important component nowadays in order to improve the productivity, the efficiency while increasing the availability of machines and different equipment The use of digital twin as a feature in mining industry has shown promising results to deal with its multiple challenges such as maintenance, production, energy consumption. In this paper, a comparative study is presented of developed digital twins in different applications such as energy management, manufacturing, industrial and campus, the goal is to design a conceptual architecture of a proposed digital twin within presenting a case study of a stacker machine in the experimental open pit mine of Benguerir. This design presented by 4 layers: physical, Data pre-processing, Edge computing, and cloud data, the goal is to work on interactions between these layers to enhance the performance of different mining machines. The case study elucidates a practical result on Stacker machine used in mining industry simulating the mechanism and the automatism of this system. This developed digital twin will be used to simulate critical scenarios and see the behavior of the stacker components in order to optimize the usage of this machine increasing its availability and reliability.

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.

In the mining industry, the use of digital transformation has become increasingly important to improve the productivity, efficiency, and availability of machinery and equipment. The implementation of digital twin technology has shown great potential in tackling issues related to maintenance, production, and safety in the industry. This paper presents a case study on the development of a digital twin-enabled monitoring system for mining haul trucks with expert system integration. The system is designed to provide real-time tracking and monitoring of haul trucks, diagnose potential anomalies, and troubleshoot issues using a statistical process control algorithm. The proposed framework encapsulates five layers: physical layer, data acquisition layer, data management layer, applications layer, and decision support layer. The study presents the materials and methods used in implementing the digital twin framework and discusses the results obtained.