State Monitoring Research Articles

Simulation model for rendering and analyzing the prediction of electric vehicle energy consumption in Matlab/Simulink

Abstract: This work presents the development of a simulation model for predicting the electricity consumption of electric vehicles (EV), with a focus on creating a model that enables precise monitoring of the electric vehicle battery's state. A simple method is provided for predicting the state of charge (SoC) of electric vehicle batteries without computational complexity and other processes. Relevance of the study is clearly demonstrated through the ease of adaptability to all types of electric vehicles, using reliable methodologies for predicting the energy consumption of electric vehicle. This approach aims to contribute to the advancement of the technological infrastructure of electric vehicles, their chargers, and routing application software.

Monitoring and modelling the deformation of an aluminium prototype mould insert under different injection moulding and clamping conditions

The conventional material of prototype injection moulds is aluminium. Well-established techniques are available on the applicability of such moulds, yet their operational state is not analysed comprehensively. State monitoring has huge benefits because the mould can be protected from excessive loads and deformations and product quality can be monitored in real time. In this paper, we measured the operational strains, cavity pressure and temperature distribution of prototype injection moulds with the traditional state monitoring method, and analysed the combined effect of holding pressure and clamping force on the operational deformations and cavity pressures. Increasing the clamping force and the holding pressure results in higher operational strains and cavity pressures. The precision of core shift analysis was tested if it can predict operational deformations of aluminium injection moulds. Core shift analysis could accurately predict the maximal deformation but it is inaccurate regarding the time when it occurs. A novel modelling method was also introduced where pressure and temperature results of the injection moulding simulation can be imported into finite element (FE) mechanical simulations. This way, mould deformations can be modelled in mechanical FE simulation environment. The measured operational deformations were compared both with core shift analysis results and with the coupled IM-FE simulation results. Core shift simulation can predict maximal deformations with adequate precision but it does not consider thermal expansion effects properly. In order to improve the accuracy of the simulations, a coupled IM-FE method is necessary, where excellent agreement can be found between the measurements and the calculations. The new, combined modelling technique can improve the accuracy of mould deformation modelling because additional mould material models become available in a professional finite element software. It can be especially useful in the modelling of polymeric mould inserts where stiffness is heavily temperature-dependent and the material creeps.

Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries

Unraveling the kinetic behavior inside the battery is essential to break through the limitations of mechanistic studies and to optimize the control of the integrated management system. Given this fact that the battery system is multi-domain coupled and highly nonlinear, an improved lumped parameter multi-physical domain coupling model is first developed to capture the electrical, thermal and aging characteristics of the battery in this paper. On this basis, an adaptive multi-timescale decoupled identification and estimation strategy is proposed based on the quantified timescale innovation, which realizes the online monitoring of the battery state and the accurate identification of the model parameters. The specific idea is that the decoupled identification of kinetic parameters inside the cell, the terminal voltage prediction and the real-time monitoring of the internal temperature with the online estimation of the available capacity are distinguished under different timescales. Meanwhile, the response time characteristic of the different kinetics is extracted and analyzed as a distinction between the coupled internal electrochemical processes. In this idea, four functionally different sub-observers are developed independently. Significantly, adaptive time-scale driven methods designed based on the fundamental timescale of the system, the amount of variation of the state of charge, and the amount of transfer charge are used separately for the observer implementation at different timescales. In addition, the coupling of the fast and slow kinetic parameter discriminators is achieved by diffusion voltage, and the internal temperature observer as well as the available capacity observer are coupled to each other based on the estimation results. Experimental results for two long-time operating conditions at 5, 25 and 45 °C show that the proposed strategy has fast convergence and reliable accuracy in monitoring the battery state characteristics. Compared with the traditional fixed timescale algorithm, the proposed multi-physics domain coupling modeling strategy based on independent timescale driven design is more competitive in practical embedded applications.

A Convolutional Neural Network Model to Detect Stress Levels in Electroencephalogram Signals

Chronic stress is recognized as a major contributing factor to the impairment of people's health and quality of life due to the negative impacts it causes both in personal and professional life. There is a tendency for the increasing of concerns about mental health and investments in healthcare in the coming years and this fact has motivated the conduction of several studies that aim to develop technological solutions that can be applied for monitoring of people's mental state to early detection and treatment of stress. Machine Learning and Deep Learning techniques have proven to be a promising alternative to provide technology to help solve the challenges in the medical field. This study presents a Convolutional Neural Network (CNN) model for classifying stress levels in data from electroencephalogram signals. The model was trained with a dataset generated from an experiment in which individuals were monitored by electroencephalogram while performing stress-inducing tasks. The model achieved an accuracy rate of 98.95% in predictions and presented an adequate balance between performance and computational costs. The results obtained demonstrate the potential of Deep Learning techniques as tools to aid in the health monitoring and diagnosis. Key Words: machine learning, deep learning, mental stress, electroencephalogram

Broadband Noise Exposure Psychoacoustic Effects Evaluation in Healthy Volunteers

The noise generated by life support systems is one of the factors that continuously affects the astronauts during their stay on the International Space Station. Long-term monitoring of the astronauts auditory system state demonstrates the possibility of developing both temporary and permanent (irreversible) shifts in hearing thresholds. In addition, it has been shown that prolonged noise exposure can adversely affect the quality of the operator’s activity and efficient performance. However, to date, the topic of psychoacoustic effects caused by noise exposure has not been studied enough. An experimental evaluation of the “non-auditory” effects of “white” noise exposure with an 85 dB intensity and 2 hours duration in healthy volunteers with normal hearing was carried out. The results indicate the negative noise impact on concentration, the amount of RAM and its use efficiency, the cognitive processes associated with the recognition and incoming information analysis speed. In addition, a significant change in the parameters of the volunteers bioelectrical brain activity was noted: an increase in the power of α- and β-rhythms and a decrease in the power of θ- EEG rhythms under the noise influence. Taking these data into account, the authors suggest a decrease in the operator’s activity quality of the volunteers in noise exposure conditions.

State monitoring of switching power supply in power system based on time series voltage waveform and image analysis

AbstractThe switching power supply is widely used in the secondary equipment of the power grid. The failure of the switching power supply will cause the protection device to malfunction, leading to a risk of large‐scale power failure. In this paper, a real‐time monitoring method based on only output voltage information is proposed. The experiment shows that this parameter‐reduced approach, relying only on voltage information, outperforms the existing method in detection accuracy.

Extraction and Application of Hydraulic Support Safety Valve Characteristic Parameters Based on Roof Pressure Data

The safety valves of powered supports control the maximum working resistance, and their statuses must be known to ensure the safety of both the support and the overlying strata. However, the inspection of powered support valves involves manual or semiautomated operations, the costs of which are high. In this study, an extreme point extraction method was developed for the determination of the characteristic parameters of safety valves using roof pressure data, and a safety valve state monitoring module was constructed. Using the longwall face of 0116306 with top coal caving in the Mindong Mine as an example, the characteristic parameters of the safety valves were extracted, including the peak, reseating, and blowdown pressures, as well as the recovery and unloading durations. The results of the field tests showed the following: (1) The amplitude threshold method based on extreme points can be used to accurately extract characteristic parameters, and the distribution of the characteristic parameters of the safety valves follows either a Gaussian or an exponential distribution. (2) The mining pressure analysis results, derived from the characteristic parameters, closely align with the in situ mining pressure observations. This method can be used for the online monitoring of safety valve conditions, increasing the operational efficiency and quality of safety valve inspections.

Fault Diagnosis of Vibration Sensors Based on Triage Loss Function-Improved XGBoost

Vibration sensors are prone to bias, drift, and other failures. To avoid misjudgments in state monitoring systems and potential safety accidents caused by vibration sensor failures, it is significant to diagnose the faults of vibration sensors. Existing methods for vibration sensor fault diagnosis are primarily based on Deep Learning, but Extreme Gradient Boosting stands out due to its excellent interpretability, and compared to other ensemble learning algorithms, it boasts superior accuracy and efficiency. Therefore, a vibration sensor fault diagnosis method based on Extreme Gradient Boosting is proposed to diagnose seven common types of faults in vibration sensors. To prevent the model from being overwhelmed by simple negative cases during training, a new loss function named Triage Loss is designed to improve the classifier’s performance. The vibration sensor fault diagnosis has confirmed the efficacy and practicality of the suggested approach. The experimental results indicate that the training of the model done using Triage Loss outperforms the training model done using the default loss function, with a maximum improvement of 5.4% accuracy, 5.45% in the F1-score, and 9.87% in the mean Average Precision under different fault rates.

Examining the Risk of Summertime Overheating in UK Social Housing Dwellings Retrofitted with Heat Pumps

The UK government has announced its ten-point plan to annually install 600,000 low-carbon heat pumps by 2028. However, there is a lack of evidence showing potential overheating risk in dwellings retrofitted with heat pumps. This paper examines the prevalence and magnitude of summertime overheating across 24 naturally ventilated social housing dwellings retrofitted with ground source heat pumps (GSHPs). The dwellings are located in a socially deprived area in Oxford (UK). The empirical study included longitudinal monitoring of indoor temperatures in the living rooms and bedrooms during the non-heating seasons of 2021 and 2022 (May–September), which included a record-breaking heatwave in July 2022. Indoor temperature and CO2 levels in bedrooms were monitored across a subset of six dwellings alongside the monitoring of window opening state in three bedrooms to understand the effect of natural ventilation in removing excess heat. About 136 thermal comfort surveys were conducted to ascertain the subjective responses of residents. Overheating risk assessment was carried out using CIBSE static and adaptive methods, which revealed that summertime overheating was prevalent across half of the dwellings in the non-heating season of 2022, as compared to 17% overheated dwellings in 2021. Bungalows with upgraded cavity wall insulation and top floor flats facing south and south-west had a propensity to overheat. The variation in indoor temperature and CO2 levels across a small sample also indicated the relationship between overheating and residents’ behaviour. Given that the majority of the dwellings were occupied by retired elderly people with low incomes who are vulnerable to heat and cannot afford active forms of cooling, it is vital to deploy passive design measures, such as appropriate shading devices that are suitable for a heating-dominated climate and enhanced ventilation, as part of home energy retrofits. Implementing reversible heat pumps coupled with solar PVs can provide cooling during heatwaves while delivering low-carbon heat in the winter.

Research on time sequence prediction of supersonic cascade flow field based on compressed sensing artificial neural network

The aviation engine that achieves efficient energy conversion is the core power equipment for low-carbon navigation, and the thermal components represented by the combustion chamber greatly affect the stable state of the compressor cascade flow field. Accurate, comprehensive and promptly monitoring of the compressor cascade flow state is related to the safe operation of the engine. A flow field time sequence prediction framework named compressed convolutional gate recurrent unit (CC-GRU) was proposed in this study to predict future supersonic cascade flow parameters based on the flow field at previous moments. CC-GRU embedded convolution into gate recurrent unit (GRU) to deal with the complex spatial-temporal behavior of supersonic cascade flow field. Firstly, unsteady numerical simulations driven by linear changes in back pressure were carried out, and the dataset for model training and validation was obtained to verify the feasibility of time sequence prediction of the cascade flow field. The verification results indicate that the framework can comprehensively predict the flow field with high back pressure in the future according to the flow field with low back pressure in the past period, and further validated the effectiveness of the proposed time sequence prediction model in ground wind tunnel experiments. The CC-GRU model can accurately capture the fine shock wave structure and flow separation zone, with a relative error of less than 10% in predicting the pressure field, and is mainly concentrated within the shock wave structure. Therefore, this research provides new research perspective and technical support for comprehensive and promptly condition monitoring of supersonic cascade flow field.

Optimal sensor placement for permanent magnet synchronous motor condition monitoring using a digital twin-assisted fault diagnosis approach

Efficient health monitoring for identifying and quantifying damages can substantially improve the performance and structural integrity of engineered systems. Specifically, new advances in sensing technologies have pushed the research of large sensor networks to monitor complex mechanical structures. Given the need for health state monitoring, designing an optimal sensor framework with accurate detectability of failure modes has great significance. However, there is often little to no experimental data available for newly proposed mechanical systems; so a digital-twin method would make fault detection feasible for this applications. In this paper, a data-driven reliability-based design optimization (RBDO) approach is employed for sensor placement and fault detection of a permanent magnet synchronous motor (PMSM), which is a relatively new system for high power engineering applications. This system suffers from inter-turn and inter-phase short-winding faults, which can cause catastrophic failure of the whole structure. For PMSMs, current sensing and magnetic field sensing can be utilized for the detection of faults, but actual sensor placement has not been considered in recent literature. In this study, the first step is to create an FEA model of the PMSM for the simulation of faults, which serves as the digital twin. Next, a data-driven approach is implemented for sensor placement and classification of faults. The proposed method utilizes distance clustering for identification of various failure modes, which is suitable for many applications due to its high accuracy and computational efficiency. In addition, a genetic algorithm is implemented to determine the minimum number and optimal placement of sensors. This framework simultaneously searches for the optimal placement of sensors while training the classifier for detectability of system health states. Ultimately, the proposed methodology shows convergence to a solution with high accuracy for detection of faults, and is demonstrated on the novel system of a PMSM with magnetic field sensors.

Development of a System for Monitoring the State and Pollution of Land Surface Water in the Design Area of the Nizhneboguchanskaya HPP

On the Angara River, there is already a cascade of four hydroelectric power plants. It is planned to build a new dam above Boguchany — Nizhneboguchanskaya hydroelectric station. This creates an additional burden on the river ecosystem. To record the initial state of the Angara River and track the dynamics of indicators, a program for monitoring the state and pollution of land surface waters in the design area of the Nizhneboguchanskaya hydroelectric power station was developed. The location of observation points was determined by taking into account the existing network of state monitoring of the investigated area of the Angara River and the analysis of the sources of anthropogenic pollution of the investigated area of the Angara River. It was proposed to make adjustments to the existing state monitoring system. It is necessary to add new observation points, expand the range of hydrochemical components and add hydrobiological studies. It is planned to establish 12 observation points and 16 sites. Sampling frequency — up to seven times a year in characteristic phases of hydrological regime. Hydrological studies should be carried out during the growing season (June — September).

Forecasting stability and growth pact compliance using machine learning

AbstractThe 2011 reform of the Stability and Growth Pact (1996) strengthened the European Commission's monitoring of EU member states' public finance. Failure to comply with the 3% limit on public deficit triggers an audit. In this paper, we present a machine learning based forecasting model for compliance with the 3% limit. We use data from 2006 to 2018 (a turbulent period including the Global Financial Crisis and the Sovereign Debt Crisis) for the 28 EU member states. After identifying 8 features as predictors among 138 variables, forecasting is performed using a support vector machine (SVM) algorithm. The proposed model achieved a forecasting accuracy of nearly 92% and outperformed the logit model used as a benchmark.

Scheduling Optimization of Printed Circuit Board Micro-Hole Drilling Production Line Based on Complex Events

The interdependence between the scheduling method and the production efficiency of a micro-hole drilling production line for printed circuit boards (PCBs) holds significant importance, necessitating the optimization of such a production line’s scheduling. Consequently, this research paper presents a scheduling optimization approach for the micro-hole drilling production line of a PCB, utilizing complex events as its foundation. Initially, a complex event model was constructed to establish correlations among extensive production line data. Subsequently, the typical complex events associated with the micro-hole drilling production line of a PCB were defined, thereby enabling the all-around monitoring of the operation state of such a production line. Furthermore, this study presents the establishment of a production scheduling model for PCB micro-hole drilling. With the goal of minimizing the maximum completion time, the catastrophe genetic algorithm was used to solve the initial scheduling scheme of the printed circuit board micro-hole drilling production line. The reliability and effectiveness of the catastrophe genetic algorithm in solving the hybrid-driven production scheduling problem of complex events were verified. Dynamic scheduling was performed when three complex events occurred in the production line: emergency order insertion, abnormal equipment operation, and tool failure. The scheduling optimization rate after identifying the emergency insertion event could reach 25.1%. The scheduling optimization rate of the production equipment operation event was related to the specific failure time of the equipment. The scheduling optimization rate after identifying the tool failure event could reach 25%. Rescheduling immediately after identifying the tool failure event could exert no effect on the initial scheduling process. It was proven that the identification and rescheduling of complex events can improve the production efficiency of a PCB micro-hole drilling production line.

Vehicle State Monitoring and Fault Detection System for Unmanned Ground Vehicles (UGV) using Markov Models

This research presents a novel fault detection and diagnostics system for unmanned ground vehicles (UGVs) by combining Markov models representing the vehicle's navigation, kinematic behavior, and vehicle dynamics systems. Existing studies do not specifically address the challenges related to UGVs and their complex subsystems or the incorporation of weather and environmental condition data. The proposed system leverages environmental and weather condition data to monitor the UGV's state and detect anomalies in its behavior. By predicting the probability of faults such as collisions, sensor damage, and other malfunctions, the system aims to enhance the safety, reliability, and performance of UGVs. The research will demonstrate the effectiveness of the proposed methodology through case studies and performance evaluation, highlighting its potential application in various real-world scenarios. This work contributes to the ongoing research in prognostics and health management, particularly for autonomous systems, by providing a new approach to fault detection and diagnostics in UGVs.

О моделировании гидродинамического режима течений в системах речных русел в период прохождения волны половодья на примере участка р. Оби

Рассмотрен пример численного моделирования прохождения волны половодья на участке верхней и средней Оби ниже плотины Новосибирской ГЭС. Расчеты выполнены с использованием численной модели для описания распространения и трансформации волн половодий и паводков в системе русел (STREAM), разрабо танной в ИВЭП СО РАН. Модель основана на одномерных уравнениях Сен-Венана. Система русел моделируется в виде графа типа дерево, в вершинах которого за даются условия сопряжения основных гидродинамических параметров. Приведена приближенная численная оценка порядка точности метода решения. Результаты моделирования хорошо согласуются c данными наблюдений. The goal of the study is to analyze the possibility of using the numerical model STREAM for performing research on the passage of a flood wave in a system of channels. The model was developed at IWEP SB RAS on the basis of one-dimensional Saint-Venant equations. It allows describing the flow regimes in channels, represented as a directed graph of the tree type. In the nodes of the graph (channel gates), the conjugation conditions for the main hydrodynamic parameters flow rates and water levels are set. The finite difference method is used to solve the system of equations. The difference scheme has a second order of accuracy in space and a first order in time. An approximate numerical estimate of the order of accuracy of the model using the Runge method is obtained. Numerical simulation of the Ob river section under the conditions of a high-water year 2014 has been carried out. To construct the morphometric model of the graph, data from the pilot map of the river and data of the expedition studies of IWEP SB RAS were used. The hydrological information is taken from the open access source “Automated information System of State monitoring of water bodies”. As a simulated object, a section of the Ob river from the Novosibirsk hydroelectric power station to Alexandrovskoye village is taken. The length of the site is 1210 km. The flow rates of six tributaries are taken into account. The graph consists of 12 segments. In the closing section, a boundary condition is set for water flow in the form of a flow rate versus level. To estimate the influence of the boundary condition on the simulation results, two water flow curves were constructed according to the data of 2013 and 2014 years. Qualitative and quantitative estimates of the difference in levels using two different curves, as well as comparison with the measurement data, were obtained. It is shown that the calculation method provides an acceptable agreement between the calculated values of water levels and the observational data. The possibility of using the water flow curve according to the data of the previous year as a boundary condition in the closing gate is demonstrated.

Multimodal and flexible hydrogel-based sensors for respiratory monitoring and posture recognition

The accurate monitoring of respiratory events and human motion states holds paramount importance in the realm of health surveillance and disease prognostication. An exquisitely precise, multifaceted, portable, and environmentally resilient sensor designed for health monitoring would undeniably be of utmost desirability, despite its persisting as a formidable challenge. Here, we propose a breath monitoring and posture recognition system that utilizes hydrogel electrolytes based on polyvinyl alcohol, sodium alginate, and starch, suitable for supercapacitors and multimodal wearable sensors. The multimodal smart sensors can independently detect mechanical and thermal changes through the output signals of capacitance and resistance, respectively. Moreover, we have cultivated an artificial neural network to achieve a finger-pressing posture recognition accuracy of up to 99.259%. Our hydrogel sensors have also been successfully employed in the diagnosis of obstructive sleep apnea syndrome. The flexible electronic device derived from this study exhibit a plethora of functionalities, thereby affording a novel perspective for the design and fabrication of advanced flexible electronic contrivances that find applications across diverse domains such as medicine and virtual reality.

Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.

Monitoring of the intrauterine state of the fetus. Question history. New possibilities of phonocardiography

The problem of decreasing perinatal mortality is one of the pressing problems in modern obstetrics. Unfortunately, current methods of monitoring the intrauterine state of the fetus that are at the disposal of an obstetrician – gynecologist (сardiotocography, Doppler velocimetry) do not guarantee fetal wellbeing in the near-term outlook, and the number of tests is limited due to safety concerns. Consequently, there is ongoing search for alternative methods of obtaining information about the intrauterine state of the fetus (phonocardiography, electrocardiography). Using IT and mathematical data analysis has considerably enlarged the phonocardiography potential, including implementation of remote monitoring of the fetal health state.A Tomsk-based company Diagnostika + LCC developed software and hardware appliance FetalCare aimed at 24-hour monitoring of the intrauterine state of the fetus based on audio data on the fetal cardiovascular system. Cardiointervalograms (CIG) obtained by phonocardiography allow to estimate the state of the fetus based on standard assessment criteria: basal heart rate, heart rate variability, presence of accelerations and decelerations, short-term variation (STV), and long-term variation (LTV). The developed appliance is non-invasive, relatively cheap, portable, and safe both for the mother and the fetus.

An Eye-Fixation Related Electroencephalography Technique for Predicting Situation Awareness: Implications for Driver State Monitoring Systems.

This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving. Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems. Thirty participants performed three simulated automated driving tasks. After each task, the Situation Awareness Global Assessment Technique (SAGAT) was deployed to capture their SA about key elements that could affect their takeover task performance. Participants eye movements and brain activities were recorded. Data on their brain activity after each eye fixation on the key elements were extracted and labeled according to the correctness of the SAGAT. Mixed-effects models were used to identify brain regions that were indicative of SA, and machine learning models for SA assessment were developed based on the identified brain regions. Participants' alpha and theta oscillation at frontal and temporal areas are indicative of SA. In addition, the FRBP technique can be used to predict drivers' SA with an accuracy of 88% using a neural network model. The FRBP technique, which incorporates eye movements and brain activities, can provide more comprehensive evaluation of SA. Findings highlight the potential of utilizing FRBP to monitor drivers' SA in real-time. The proposed framework can be expanded and applied to driver state monitoring systems to measure human SA in real-world driving.