Subsystem Level Research Articles

Multi-level risk classification of distributed embedded software failures for autonomous systems

With increasing autonomy in systems, the role of software becomes more prominent as it overtakes human operator functions. The software in autonomy differs from automation with respect to functionality, implementation, and complexity, and software failures contribute to system and operational risk. Such failures, however, are often not sufficiently catered for in current risk assessments and mitigation processes, as they are challenging to identify and quantify, in particular, in the early conceptual design phase. Software reliability is not the same as software safety, as the latter encompasses the context and use of the software, as well as interactions and potential cascading failures to hardware, humans, and the environment. It is also difficult to investigate cascading effects on the system that may follow from software failures. The objective of this paper is to propose a novel classification taxonomy to support a more thorough identification of software failures for systems with different degrees of autonomy, as well as for software implementation techniques. The risk from software is interwoven into the design, development, validation, and verification processes, impacting safe operation. The proposed taxonomy can be used iteratively from the early design phase as the detailed design concepts evolve. The level of abstraction for system and software functions decreases with the design and development process. The validation and verification processes must ensure the software’s safety and reliability on different system abstraction levels. The software taxonomy in this paper includes relevant causes, consequences, and process relationships, and has been created based on existing industry classifications, research, and system models. A case study applying the taxonomy to navigation and collision avoidance functions on the subsystem level of a Maritime Autonomous Surface Ship (MASS) is performed. Software properties extracted from existing systems and knowledge are transformed into a functional model. Each software failure is then described in the context of the system level valid for the design, development, validation, and verification processes for MASS. The overall outcome of the paper may contribute to the safer design of systems through enhanced identification of potential hazards and software failures, leading to improved risk assessments and, as such, a better basis for defining more efficient safety requirements for autonomous systems from the early system development. Even though the paper exemplifies the taxonomy and classification by focusing on MASS, the work has relevance to other types of software-intensive systems.

Hierarchical design method for hybrid equipment used in friction stir welding

The proposed hybrid equipment for friction stir welding (FSW) focuses on balancing heavy load capacity and compact size. The kinematics and static compliance of the chosen mechanism are modeled and analyzed. Comprehensive objectives, including kinematic performance, static compliance, and overall machine quality, are considered. On this basis, a scale optimization domain and a compliance-constrained domain are established. A multi-layer grid optimization strategy is then implemented, progressing from the subsystem level to the entire machine to refine the design. Optimal parameters are identified through a detailed search in the size domain. This leads to the definition of three-dimensional design parameters for creating a digital prototype of the hybrid equipment, resulting in a structured hierarchical design method and process.

The cognitive critical brain: modulation of criticality in perception-related cortical regions

The constantly evolving world necessitates a brain that can swiftly adapt and respond to rapid changes. The brain, conceptualized as a system performing cognitive functions through collective neural activity, has been shown to maintain a resting state characterized by near-critical neural dynamics, positioning it to effectively respond to external stimuli. However, how near-criticality is dynamically modulated during task performance remains insufficiently understood. In this study, we utilized the prototypical Ising Hamiltonian model to investigate the modulation of near-criticality in neural activity at the cortical subsystem level during perceptual tasks. Specifically, we simulated 2D-Ising models in silico using structural MRI data and empirically estimated the system's state in vivo using functional MRI data. We first replicated previous findings that the resting state is typically near-critical as captured by the Ising model. Importantly, we observed heterogeneous changes in criticality across cortical subsystems during a naturalistic movie-watching task, with visual and auditory regions fine-tuned closer to criticality. A more fine-grained analysis of the ventral temporal cortex during an object recognition task further revealed that only regions selectively responsive to a specific object category were tuned closer to criticality when processing that object category. In conclusion, our study provides empirical evidence from the domain of perception supporting the cognitive critical brain hypothesis that modulating the criticality of subsystems within the brain's hierarchical and modular organization may be a fundamental mechanism for achieving diverse cognitive functions.

Decentralized Control of Complex Systems: Lyapunov Function Approach

In this contribution, we generalize existing methods for decentralized control design, providing a unified methodological framework that applies to linear continuous and discrete-time complex systems, as well as certain classes of nonlinear complex systems. Our approach leverages the direct connection between the stability properties of the overall complex system and those of its individual subsystems. By conducting the entire controller design process at the subsystem level, we circumvent the need for explicit interconnection values. Through numerical examples, we demonstrate that the proposed method ensures asymptotic stability of the full complex system within a specified region, and also guarantees stability of the isolated subsystems. In particular, we obtain quantifiable stability margins (e.g., γc bounds) and closed-loop eigenvalue placements that verify the effectiveness of the design. These results highlight not only the method’s theoretical robustness, but also its practical significance in simplifying the design process, reducing computational overheads, and enhancing scalability for large and interconnected engineering systems.

Investigating the Reliability of Heating, Ventilation, and Air Conditioning Systems Utilized in Passenger Vehicles

A Heating, Ventilation, and Air Conditioning (HVAC) system is often utilized in passenger vehicles to enhance the comfort of both the driver and the passengers. The reliability of an HVAC system refers to the probability that a component within the system will fulfil its intended function during a specified timeframe while operating according to the predefined operational and environmental conditions. Conducting a reliability analysis for the HVAC system of a passenger vehicle is crucial to ensure safety, comfort, cost-effectiveness, and a positive standing. A methodology for analyzing the reliability analysis of a HVAC system using field failure data were developed to identify the critical failure modes, components, and subsystems. A detailed Pareto analysis was applied at subsystem and failure mode levels in order to prioritize them accordingly to their failure frequency. The analysis showed that the A/C evaporator and blower front sides were observed to be the most critical subsystems, contributing to approximately 50% of all failures. Furthermore, the leakages at the joints and vibrations are the primary failure modes of the HVAC system. The Weibull++ software package (version 2021) was used to estimate the best-fit probability distributions for each subsystem and system reliability modelling using a Reliability Block Diagram. The results show that the exponential distribution fits well for several subsystem’s Time-To-Failure (TTF) data and show that the failures were random and due to external reasons.

Factors Influencing Water Resource Levels Under the Water Resource Carrying Capacity Framework: A Dynamic Qualitative Comparative Analysis Based on Provincial Panel Data

Most existing evaluation frameworks for water resource carrying capacity (WRCC) neglect the interdependencies between subsystems. To fill this gap, we introduce a dynamic qualitative comparative analysis (QCA) model to evaluate WRCC and apply it to a vital economic development corridor, the Yangtze River Economic Belt (YREB). Ecological, social, and economic subsystems are defined as condition subsystems, while the water resource subsystem is defined as the outcome subsystem. The entropy weight method is used to calculate and calibrate the comprehensive score of each subsystem. By analyzing the necessity of a single condition subsystem and the sufficiency of condition subsystem configuration via a dynamic QCA, we qualitatively analyze the impact extent and pathways of the ecological, social, and economic subsystems on the water resource subsystem within the WRCC framework. The results reveal generally stable water resource levels despite regional variances, thereby pinpointing the influence pathways, including ecological–social and ecological–economic configurations. The 2011–2015 period saw poor stability, which subsequently improved until 2019 before declining in 2020 in the YREB. The middle-reach urban cluster showed the highest stability, which was less impacted by condition subsystems. These findings could enable provinces and municipalities to tailor policies and enhance subsystem levels for better water resource management.

The relevance of shock absorber friction on the total friction of a MacPherson suspension system during lateral manoeuvres

The friction potential of the suspension is typically considered to be invariant as the vertical dynamics of a vehicle are analysed. However, the current research on MacPherson suspensions indicates a strong dependence of the suspension friction on the present driving situation, which is primarily characterised by the wheel deflection relative to the chassis, and the tyre forces. Although it is well known that the shock absorber is generally subjected to side loads that can vary and initiate a certain amount of friction, the precise contribution of the shock absorber friction to the total suspension friction has yet to be investigated in detail. It is therefore essential to conduct a quantitative study on the importance of the shock absorber in the context of specific driving situations regarding the development of new, friction optimised shock absorber generations. The present study investigates the relationship between total suspension friction and the friction of the shock absorber, with a particular focus on the case of wheel lateral forces that aim to represent a simplified cornering manoeuvre of the vehicle. This is realised by using a multi-body simulation approach and a strain gauge application in order to estimate the side force at the shock absorber’s top mount. Subsequently, the shock absorber friction is determined using a test setup that considers the realistic load and mounting situation from the perspective of the MacPherson suspension subsystem. The combination of simulation and shock absorber friction measurement is sufficient to fully explain the characteristic friction trend on suspension (subsystem) level. The contribution of the shock absorber friction to the total suspension friction is determined to be a maximum of 80%, emphasising the importance of considering the side force at the shock absorber while aiming for suspension friction minimisation and ride comfort performance enhancement. On the other hand, the shock absorber’s contribution is also observed to be limited depending on the actual wheel force, indicating that the friction of the other components remains a crucial factor in the MacPherson suspension concept.

APPROVAL OF ORGANIZATIONAL AND PEDAGOGICAL CONDITIONS FOR THE FORMATION OF THE READINESS OF FUTURE FIRE TRAINING INSTRUCTORS FOR PROFESSIONAL ACTIVITIES WITH THE EMPHASIZED USE OF SPECIAL PHYSICAL TRAINING TOOLS

The article is devoted to the topical issues of forming the readiness of future fire training instructors for professional activity with an emphasis on the use of special physical training tools. According to the results of the analysis of scientific-methodical, special and reference literature (monitoring of specialized Internet resources), the members of the scientific research group found that studies that comprehensively reveal the peculiarities of the organization of the system of training future instructors in fire training (including with an accented use of means physical and special physical training) are rare. Taking into account the realities of today, this proves the timeliness and relevance, as well as the practical component of the chosen direction of scientific research. During the research and analytical work, the organizational and pedagogical conditions for the formation of the readiness of future fire training instructors for professional activity with an emphasis on the use of special physical training tools were adjusted and tested. The organizational and pedagogical conditions developed by the members of the research group were implemented during three stages: organizational and diagnostic, formative and constant (with appropriate educational, methodological and technical support). For the first time, with the use of modern scientific tools, indicators at the level of the biomechanical control subsystem were obtained. Prospects for further research in the chosen direction of scientific intelligence include the development of a methodology for the formation of military applied skills (techniques) of shooting with automatic small arms of recruits of the National Guard of Ukraine at the initial stage of primary military professional training.

Aspect based sentiment analysis of consumer reviews using unsupervised attention neural framework

Consumer reviews posted on e-commerce platforms offer an assessment of their opinion and sentiments about various aspects of a product or service. The aspects may belong to a specific category at subsystem and component level. The irrealis often associated with an opinion term, if not handled carefully, may distort the sentiment scores. This paper introduces a clustering and negative-sampling backed feed-forward Unsupervised Attention Deep Neural Framework for Opinion-Aspect-Category-Irrealis-Sentiment (UAN-OACIS) Quintuple extraction. This framework splits the five subtasks into two stages. In the first stage, aspects and opinions are extracted using word embedding-based multi-layer Attention Deep Neural Network (DNN) models; while the second stage projects corresponding categories, irrealis, and sentiment scores by creating a hash table and a couple of algorithms. We also propose a POS-Tag-based algorithm to make an irrealis dictionary to suggest sentiment modification percentage scores; and another algorithm to modify existing sentiment-lexicon based on domain-specific words. We compare UAN-OACIS with four unsupervised and two semi-supervised approaches at subtask level, and two supervised approaches at quadruples level excluding irrealis, and have got comparable results. Since no method is available for overall comparison at quintuple level, we additionally study the impact of irrealis on sentiment scores and observe substantial improvement compared to state-of-the-art lexicons methods.

Decentralized control of nonlinear complex systems

Abstract In the paper, a novel approach to decentralized controller design for nonlinear systems is introduced. The proposed method is based on the relationship between the stability of complex nonlinear systems and the stability of their subsystems. The design procedure of the decentralized controller consists of three steps. In the first step, the stability of the complex nonlinear system is calculated. In the second step, stability conditions at the subsystem level are obtained such that guarantee the stability of the complex nonlinear system. Finally, in the third step, a controller design method is used to ensure that the subsystem stability conditions obtained in the second step are met. As an example, to better understanding the proposed method two simple nonlinear models are used to demonstrate the effectiveness of the proposed method.

A novel importance measure considering multi-constraints for RAP optimization of 1-out-of-n subsystems with mixed redundancy strategy

Intelligent optimization algorithms are the mainstream approach to solving redundancy allocation problems (RAP) with challenging features. Since importance measures (IM) can identify critical components, the combination of IM-based local optimization and intelligent algorithms has wide applications in various optimization problems; however, it is less studied in RAP. Existing IMs also failed to address both the objective function and multiple constraints like cost and weight; this may result in an imprecise identification of critical subsystems for RAP optimization. This paper considers a RAP with a mixed strategy, i.e., active and standby strategies can be applied to a subsystem simultaneously. Two novel IMs are proposed based on a Lagrangian function: cost-centric RAP-based importance (CRI) and weight-centric RAP-based importance (WRI). CRI (WRI) reveals the comprehensive effect of cost (weight) consumption on the system reliability and other resources. A local optimization algorithm guided alternately by CRI and WRI is presented to adjust the redundancy level of subsystems; then, this algorithm is introduced into a genetic algorithm (GA) to determine the component types and redundancy level of all subsystems. Compared with other algorithms and previous studies, the superiority of the proposed hybrid GA is demonstrated via numerical experiments and a well-known benchmark example.

A Framework of Real-time Knowledge Capture and Formalization for Model-based Design with Spoken Annotation and Design Operations

Abstract Knowledge capture without an extra burden on engineers is one of the most challenging issues in the engineering domain. This study proposes a framework of real-time knowledge capture and systematic formalization for the model-based design (MBD), the systems design approach where a simulation is a key component. The framework records the screen of the modeling tool as video data and spoken annotation in which an engineer verbally explains the rationale of the modeling conducted with Modelica-based software commonly used for MBD. The captured modeling tool log and spoken annotation are divided into chunks, each corresponding to a design operation, e.g., defining functions, defining system components, setting model elements, setting model parameters, and running simulations. gIBIS, the argumentation model for representing design rationale, represents the logical structure of the design process. We define design operation templates at the system and subsystem levels, which act as rules to convert the modeling tool log into gIBIS format systematically. We conduct a design case of a spinach harvesting machine with MBD software to verify the proposed framework. The results show that through non-intrusive recording of the design process, the proposed framework is effective in capturing knowledge that can be used to help understand how a model was built.

Coupling Coordination Relationship and Spatiotemporal Heterogeneity between Urbanization and Ecosystem Services in the Songhua River Basin

Rapid urbanization in the Songhua River Basin (SRB), a crucial ecological barrier in China and Northeast Asia, has led to the degradation of ecosystem service functions and a decline in their value, thereby posing a significant threat to regional ecological security. Clarifying the complex coupling coordination relationship between urbanization and ecosystem services (ESs) and identifying the spatiotemporal heterogeneity of their interactions will facilitate the high-quality and coordinated development of urbanization and ESs in the SRB. This study employed a systems approach, treating urbanization and ESs as overarching systems and delineating different aspects of urbanization and ecosystem service functions as subsystems within these systems. The spatiotemporal characteristics of urbanization and the ecosystem service value (ESV) in the SRB from 1985 to 2021 were revealed. The coupling coordination relationship and the spatiotemporal heterogeneity of the interactions between urbanization and ESs in the SRB at both the system and subsystem levels were analyzed using the coupling coordination degree (CCD) model and the spatiotemporal geographically weighted regression (GTWR) model. The findings indicated that during the study period: (1) The urbanization index of SRB rose from 0.09 to 0.34, while the ESV experienced a decrease from 2091.42 × 107 CNY to 2002.44 × 107 CNY. (2) The coupling coordination degree (CCD) between urbanization and ESs in the SRB at both the system and subsystem levels increased significantly, generally transitioning from the moderately unbalanced to the basically balanced stage. Areas with high CCD values were mainly distributed in ecological function areas and low-level urbanized areas, while areas with low CCD values were mainly distributed in grassland ecological degradation areas, ecologically fragile areas, resource-dependent old industrial cities, and highly urbanized areas. (3) The subsystems of urbanization had an overall negative impact on Ess, with varying trends, but the spatial distribution pattern of the interactions remained relatively stable. Conversely, the subsystems of ESs all exhibited a trend of initially strengthening and then weakening their negative impacts on urbanization, and the spatial distribution pattern was highly correlated with the spatial distribution pattern of ESV in the SRB.

Reduced-order modeling of modular, position-dependent systems with translating interfaces

Many complex mechatronic systems consist of multiple interconnected dynamical subsystems, which are designed, developed, analyzed, and manufactured by multiple independent teams. To support such a design approach, a modular model framework is needed to reduce computational complexity and, at the same time, enable multiple teams to develop and analyze the subsystems in parallel. In such a modular framework, the subsystem models are typically interconnected by means of a static interconnection structure. However, many complex dynamical systems exhibit position-dependent behavior (e.g., induced by translating interfaces) which cannot be captured by such static interconnection models. In this paper, a modular model framework is proposed, which allows to construct an interconnected system model, which captures the position-dependent behavior of systems with translating interfaces, such as linear guide rails, through a position-dependent interconnection structure. Additionally, this framework allows to apply model reduction on subsystem level, enabling a more effective reduction approach, tailored to the specific requirements of each subsystem. Furthermore, we show the effectiveness of this framework on an industrial wire bonder. Here, we show that including a position-dependent model of the interconnection structure (1) enables to accurately model the dynamics of a system over the operating range of the system and, (2) modular model reduction methods can be used to obtain a computationally efficient interconnected system model with guaranteed accuracy specifications.

Functional connectivity of the default mode network in first-episode drug-naïve patients with major depressive disorder

BackgroundAlterations in the default mode network (DMN) have been reported in major depressive disorder (MDD), well-replicated robust alterations of functional connectivity (FC) of DMN remain to be established. Investigating the functional connections of DMN at the overall and subsystem level in early MDD patients has the potential to advance our understanding of the physiopathology of this disorder. MethodsWe recruited 115 first-episode drug-naïve patients with MDD and 137 demographic-matched healthy controls (HCs). We first compared FC within the DMN, within/between the DMN subsystems, and from DMN subsystems to the whole brain between groups. Subsequently, we explored correlations between clinical features and identified alterations in FC. ResultsFirst-episode drug-naïve patients with MDD showed significantly increased FC within the DMN, dorsal DMN and medial DMN. Each subsystem showed a distinct FC pattern with other brain networks. Increased FC between the subsystems (core DMN, dorsal DMN) and other networks was associated with more severe depressive symptoms, while medial DMN-related connectivity correlated with memory performance. LimitationsThe relatively large “pure” MDD sample could only be generalized to a limited population. And, atypical asymmetric FCs in the DMN related to MDD might be missed for only left-lateralized ROIs were used to avoid strong correlations between mirrored (right/left) seed regions. ConclusionThese findings suggest patients with early MDD showed distinct patterns of FC alterations throughout DMN and its subsystems, which were related to illness severity and illness-associated cognitive impairment, highlighting their clinical significance.

Long-baseline multistatic and bistatic SAR products: Application to the RODiO mission

In-Orbit Demonstration (IOD) missions represent flight opportunities to acquire data on spacecraft and space environment and to demonstrate new techniques at system, subsystem, and payload level. Realizing this type of mission means accepting a greater level of risk. However, when very compact and relatively simple platforms like CubeSats are adopted, this becomes admissible since costs can be kept low. Distributed Synthetic Aperture Radar (SAR) is a novel Earth Observation (EO) technique for microwave imaging. It uses multiple cooperating receivers and suitable processing to enable performance and products that exceed those of the single components of the system. RODiO (Radar for earth Observation by synthetic aperture Distributed on a cluster of CubeSats equipped with high-technology micro-propellers for new Operative services) is an IOD of the distributed SAR concept. The mission is based on four, 16 U, CubeSats, that embark a receiving-only X-band SAR instrument able to collect bistatic echoes exploiting a monostatic SAR as an opportunity illuminator, i.e PLATiNO-1 mission. Distributed SAR demonstration is thus pursued in RODiO combining the principles of both multistatic and bistatic SAR. The paper, moving from mission goals, individuates different families of EO products that RODiO can demonstrate, e.g. in the contest of ground motion, digital elevation models, and ship detection. The main result is the definition of the RODiO system requirements for product generation that drive satellites and payload design.

Создание функциональной подсистемы по предупреждению и ликвидации последствий чрезвычайных ситуаций на водном транспорте

The relevance of the study is based on the need to improve the Unified State System of Prevention and Elimination of Emergencies in order to increase the safety level in case of accidents of natural and anthropogenic origins in water transport industry facilities. In the field of functional subsystems of water transport, the focus is made on eliminating marine and internal waterway oil and petroleum product spills as well as the search and rescue operations during marine emergencies. At the same time, the scope of emergencies in water transport that can be environmentally hazardous is more extensive and includes fires and explosions, discharges of non-petroleum dangerous goods (for example, coal, sulfur, chemical goods, etc.) The Unified State System is regulated in conditions of insufficient scientific substantiation of the principles of construction, organization, and use of functional subsystems. Therefore, the study aims to substantiate the necessity to establish and develop structural schemes of a new functional subsystem that includes functioning modes, management bodies, responsibility levels, logistical and financial support. For this purpose, the existing functional subsystems have been analyzed, and the development of an integral functional subsystem to prevent and eliminate emergencies in water transport has been proposed. Methods of decomposition of emergencies and generalization of information on the operation of other transports as well as the analysis of regulatory legal acts outlining certain issues of the operation of these functional subsystems have been used. The characteristics to determine the extent of emergency danger have been formulated; the detailed characteristics of levels of a functional subsystem to prevent and eliminate a wide range of various emergencies have been provided based on the example of marine water transport; the subsystem tasks for various levels of emergency have been formulated.

Large-scale group hierarchical DEMATEL method with automatic consensus reaching

Decision-making trial and evaluation laboratory (DEMATEL) is widely used because of its ability to effectively analyze nonlinear relationships between factors in complex systems. With the increasing complexity of decision-making problems, large-scale group decision-making (LSGDM) has become the norm. Most existing DEMATEL methods are only suitable for small-scale groups and simple systems. This study, therefore, proposes a large-scale group hierarchical DEMATEL method that considers consensus reaching. The DEMATEL method for LSGDM faces three challenges: large differences in knowledge structures, difficulty coordinating expert opinions, and slow group-consensus convergence. To address these challenges, first, we use hierarchical decomposition to decompose the complex system into simple systems with different levels to reduce the difficulty of decision-making in complex systems. Second, considering the limitations of expert knowledge and experience, we use the basic probability assignment function to extract the opinions of experts at different levels of subsystems and factors. Third, we divide experts into different clusters using K-means clustering to solve the problem of difficult expert-opinion coordination. Fourth, we design two types of consensuses (intrasubgroup and intersubgroup consensus) and an efficient new type of opinion autocorrection mechanism to solve the problem of the slow convergence of intragroup consensus and improve the efficiency of consensus reaching. Finally, we demonstrate the superiority of the proposed method through data analysis and method comparison.

The dynamic carbon footprint modeling for laser direct metal deposition based on processing states

The Laser Direct Metal Deposition (LDMD) process offers notable advantages and obtains wide application in aerospace, defence equipment and other industrial sectors. However, the characteristics of multiple-inputs and low-efficiency in the process will bring about high carbon footprint. Additionally, the carbon footprint fluctuates with processing states and equipment systems owing to the discontinuous process in LDMD involving the layer-by-layer deposition and the laser-powder coupling. This paper investigated dynamic carbon footprint based on the resource and energy consumption from the process-oriented standpoint in LDMD process, which quantified the processing time, power distribution and material consumption of each processing state for every sub-system. In this research, the discontinuous process was divided into effective processing state, rapid moving state and transformed delay state and the processing time of each state were estimated with process parameters. Besides, the operating state time and power distribution of five sub-systems were estimated combining the equipment triggering mechanism and the processing state time. The orthogonal experiment was carried out to deposit the 316L stainless steel powder into parts with the size of 15 mm*15 mm*10 mm under the given process parameter conditions (Vf ∈ [8,12] g/min, P ∈ [600,1000] w, Vs ∈ [500,900] mm/min). The results show that carbon footprint of deposited parts fluctuates in the range of 3624.68–15759.08 g under parameter combinations and the low carbon footprint parameter-interval is obtained. The average carbon footprint of effective processing state, the transformed delay state and the rapid moving state account for 55%, 40% and 5% respectively, while the carbon footprint of powder supply sub-system contributes to 68% among the five sub-systems. This research revealed and qualified the dynamicity of carbon footprint at the levels of processing states, process parameters and sub-systems, offering potential for carbon footprint reduction in advanced manufacturing processes.

Employee engagement index: A graph‐theoretic matrix approach

AbstractIn today's fast‐paced business environment, organizations are increasingly recognizing the critical role of employee engagement in attaining long‐term success and competitiveness. Industries increasingly utilize employee engagement systems to improve public perceptions by incorporating human resource management practices, psychological ownership, job crafting, and organizational citizenship behaviors. These elements are focused on reducing the effects of employee turnover. Therefore, this article aims to represent the factors and subfactors influencing employee engagement and develop an employee engagement index. In this research, an employee engagement index was formulated and the positive impact of factors influencing employee engagement was evaluated using a graph‐theoretic and matrix approach (GTMA). This index measures the number of factors in an organization conducive to creating an engaging environment among employees. It gives information about aspects at the system and subsystem levels. A step‐by‐step procedure for implementing methodology is provided, along with an example that may assist an organization.