Use Of Virtual Sensors Research Articles

Виртуальные датчики в автоматизированных системах управления процессом гидроочистки дизельной фракции

Petrochemical plants are equipped with many instruments and a large number of sensors that collect measurement data to control and monitor the process. At the same time, researchers began using large amounts of data to build predictive models, which they called virtual sensors. The article is devoted to the analysis of the use of virtual sensors within the framework of the hydrotreating process of diesel fractions. A classification of virtual sensors developed by the authors is presented, which helps to identify and select tools for monitoring, which helps to increase the accuracy, flexibility and efficiency of production control mechanisms. The authors detail the development process for virtual sensors, highlighting their potential as a strategic asset that can enhance technological productivity and improve enterprise competitiveness. The development of a block diagram of a control system for the diesel hydrotreating process is also covered, demonstrating the integration and use of virtual sensors to improve the specified process.

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Having garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective revolves around devising a virtual sensor for the indirect size measurement of the bead. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. The internal signals recorded, the comprising voltage, the current, the energy from the welding heat source generator, the wire feed speed from the feeding system, the traverse speed from the machine axes, and the temperature from a pyrometer located in the head were all captured through the control of the machine specially dedicated to the arc-DED process during a phase of optimizing and modeling the bead geometry. Finally, a feedforward neural network (FNN), also known as a multi-layer perceptron (MLP), is designed, with the internal signals serving as the input and the height and width of the bead constituting the output. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process. Furthermore, a neural network model is implemented with a test loss of 0.144 and a test accuracy of 1.0 in order to predict weld bead geometry based on process parameters, thus offering a promising approach for real-time monitoring and defect detection.

A Review of Gas Pumps Fault Behavior and Measurement, Diagnosis and Identification Methods Including Virtual Sensors

Almost half of all electrical pump capacity consume in three-phase induction motors and most of them use in gas pumps and compressor in processes of industries for heating, cooling, pumping, conveyors, etc. The most important role of fault detection and detection (FDD) for manufacturing equipment is an effective indicator that can identify the faulty state of a process and then take appropriate action against future failures or adverse events. The impact of proper maintenance is reflected on an especially costly type of industrial machine. To avoid reducing the efficiency of the gas station, automated fault detection and diagnostics is very important and need to study. This article aims to establish a criterion for selecting which faults can be tested under laboratory conditions or by simulation with a virtual model and to determine the features that identify those faults. This efficiency identify of faults leads to saving of energy, service and operating costs. Virtual sensors applied to gas pumps to reduce the cost associated with FDD implementation are described. Finally, several areas of improvement for the aspects reviewed have been identified: increase the use of performance indicators for FDD, new and updated studies about the health status of field heat pumps, testing methods that take into account the gradual and probabilistic nature of heat pump faults and further research in the use of virtual sensors in FDD systems.

Artificial Intelligence Strategies for the Development of Robust Virtual Sensors: An Industrial Case for Transient Particle Emissions in a High-Performance Engine

<div>The use of data-driven algorithms for the integration or substitution of current production sensors is becoming a consolidated trend in research and development in the automotive field. Due to the large number of variables and scenarios to consider; however, it is of paramount importance to define a consistent methodology accounting for uncertainty evaluations and preprocessing steps, that are often overlooked in naïve implementations. Among the potential applications, the use of virtual sensors for the analysis of solid emissions in transient cycles is particularly appealing for industrial applications, considering the new legislations scenario and the fact that, to our best knowledge, no robust models have been previously developed. In the present work, the authors present a detailed overview of the problematics arising in the development of a virtual sensor, with particular focus on the transient particulate number (diameter <10 nm) emissions, overcome by leveraging data-driven algorithms and a profound knowledge of the underlying physical limitations. The workflow has been tested and validated using a complete dataset composed of more than 30 full driving cycles obtained from industrial experimentations, underlying the importance of each step and its possible variations. The final results show that a reliable model for transient particulate number emissions is possible and the accuracy reached is compatible with the intrinsic cycle to cycle variability of the phenomenon, while ensuring control over the quality of the predicted values, in order to provide valuable insight for the actions to perform.</div>

State Estimation in a Biodigester via Nonlinear Logistic Observer: Theoretical and Simulation Approach

The state variables in a biodigester are predicted using an unstructured model, and this study offers an analytical design of a Non-Linear Logistic Observer (NLLO), subsequently comparing its performance to that of other prominent state estimators. Because of variables such as temperature, pH, high pressure, volumetric organic load (VOC), and hydraulic retention time (HRT), among others, biodigester samples can be affected by the use of physical sensors, which are not always practical owing to their sensitivity to the type of sampling and external disturbances. The use of virtual sensors represents one approach to solving this issue. In this work, we suggest experimentally validating a mathematical model, then analytically designing a novel NLLO observer, and finally comparing the results to those obtained using a sliding-mode estimator and a Luenberger observer. By including online CH4 and CO2 measurements as inputs to the proposed observer, the local observability analysis demonstrated that all state variables were recoverable. After showing how well the suggested observer performs in numerical experiments, a proof based on the Lyapunov theory is offered. The primary innovation of this study is the incorporation of a novel algorithm that has been empirically validated and has output resilience to input parametric perturbations.

Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems.

The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout.

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal–hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. This double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

Combining knowledge and historical data for system-level fault diagnosis of HVAC systems

Interdependencies among system components and the existence of multiple operating modes present a challenge for fault diagnosis of Heating, Ventilation, and Air Conditioning (HVAC) systems. Reliable and timely diagnosis can only be ensured when it is performed in all operating modes, and at the system level, rather than at the level of the individual components. Nevertheless, almost no HVAC fault diagnosis methods that satisfy these requirements are described in literature. In this paper, we propose a multiple-model approach to system-level HVAC fault diagnosis that takes component interdependencies and multiple operating modes into account. For each operating mode, a distinct Bayesian network (diagnostic model) is defined at the system level. The models are constructed based on knowledge regarding component interdependencies and conservation laws, and based on historical data through the use of virtual sensors. We show that component interdependencies provide useful features for fault diagnosis. Incorporating these features results in better diagnosis results, especially when only a few monitoring signals are available. Simulations demonstrate the performance of the proposed method: faults are timely and correctly diagnosed, provided that the faults result in observable behavior.

Control of a Vacuum Coating Process with Long Dead-Time and an Integrator: a Case Study

Abstract This paper presents the comparative analysis of different methods for the control of a characteristical industrial process with a long dead-time. Not only traditional control strategies (Smith predictor, Model-Following Controller and Generalized Predictive Control) are considered, but also an alternative approach - the use of virtual sensors.

Missions preparation and design of new algorithms for Charlie and Taipan within Thetis, a HIL simulator

The purpose of this paper is to present why and how the new simulator, called Thetis , can be used to test and validate new coordinated command algorithms. We focus on examples involving the Autonomous Underwater Vehicle (AUV) Taipan 2 developed at LIRMM-CNRS (France) and the Autonomous Surface Vehicle (ASV) Charlie developed at ISSIA-CNR (Italy). Thetis is a new real-time multi-vehicles simulator for heterogeneous vehicles. It allows Hardware In The Loop (HIL) simulations including the use of virtual sensors providing a representation of a virtual world, including the support of communication devices. After a short state-of-the-art study, we introduce the main mechanisms of our simulator and we present the specificities of our tool compared to the other existing solutions. Finally we show a set of typical missions involving Taipan 2 and Charlie, focusing on the ability of our tool to validate ongoing propositions concerning flotilla strategy.