•A novel model SIPUSUI is designed which accurately model the spread of computer virus in critical industrial networks.•Local stability analysis of the model is ascertained at equilibria points both ...for virus free and endemic spread scenarios.•Validation of the model through global stability analysis with Lyapunov function further establish its worth.•Numerical simulation reasonably validate the model accuracy for Stuxnet virus actual data.
The purpose of this study is to develop an epidemic virus model that portrays the spread of the Stuxnet virus in a critical control infrastructure after bridging the air-gap between a normal local area network and the critical network. Removable storage media plays an important role in the transfer of data and virus to the computers connected to the critical network (consisting of industrial controllers) and this can compromise the whole system. A mathematical model is formulated that incorporates these features and depicts the controlling mechanism. Disease free and endemic equilibria are analyzed in terms of the basic reproduction number R0. Global stability of disease free and endemic equilibrium points are analyzed using Lyapunov functions. Numerical simulations are performed to determine the accuracy of the proposed model for the smart Stuxnet virus which is designed to target critical industrial systems. Model shows very good resemblance with the observed real life data available for this virus. Future work may invoke interesting results and control strategies.
The lean philosophy comprises a group of revolutionary concepts and a set of innovative tools for visualizing and improving the system. However, its integration within the various areas of industrial ...engineering, as a core base, needs to be wider explored. This study builds further on the lean philosophy through focused reviews of thermodynamics and fluid mechanics what has allowed the derivation of analogies and conceptual adaptations. The study adopts the system view and orients itself as a conceptual study leading to the development of a Holistic framework for studying industrial systems. The outcomes of the study provide a structured procedure along with supporting tools and guiding schemes for studying industrial systems and open wide future research opportunities in the direct implementation as well as in the system analysis, system optimization and system design.
•Simultaneous optimization model of the industrial system design and maintenance•Maintenance optimization focuses on the development of a dynamic maintenance•Design refers to reliability, redundancy, ...maintainability and diagnostics•Simultaneous optimization is achieved using a hybrid optimization algorithm•Results show performance improvement in terms of reliability and life cycle cost
This article describes a new approach to simultaneous optimization of design and maintenance of large-scale multi-component industrial systems. This approach, in a form of an algorithm, aims to help designers in the search for solutions by characterizing the components and their architecture including maintenance issues. The aim is to improve the performance of the industrial systems by maximizing the Total Operational Reliability (TOR) at the lowest Life Cycle Cost (LCC). In the case of this research, the term "design" refers to the reliability properties of the components, possible redundancies, faulty component accessibility, and the ability to improve the component real-time monitoring architecture. The term “maintenance” refers to maintenance plan adapted to the opportunistic dynamic maintenance plan. Simultaneous optimization of design and maintenance is achieved by a two-level hybrid algorithm using evolutionary (genetic) algorithms. The first level identifies the optimal design solutions calculated relative to the TOR and the LCC. The second proposes a dynamic maintenance plan that maximizes the reliability of the system throughout its operating life.
Data-driven soft sensors are usually used to predict quality-related but hard-to-measure variables in industrial systems. However, the acceptable prediction performance mainly relies on the premise ...that training data are sufficient for model training. To acquire more training data, this paper proposes an adversarial transfer learning (ATL) methodology to enhance soft sensor learning. Firstly, a hierarchical transfer learning algorithm, which integrates a feature extraction method with model-based transfer learning, is proposed to refine the useful hidden information from both historical variables and samples. Then, a novel adversarial learning network is designed to prevent the deterioration of transferred results at each transfer learning stage. Thirdly, a Granger causality analysis (GCA)-based rationale analyzer is added to unfold the internal causality among input variables and between input and output variables simultaneously. Finally, the effectiveness of the proposed soft sensor and the rationale analyzer is validated in a simulated wastewater plant, Benchmark Simulation Model No.2 (BSM2), and a full-scale oxidation ditch (OD) wastewater plant. The experimental results demonstrate that the ATL-based soft sensor can achieve more accurate prediction in terms of RMSE and R, and the GCA-based rationale analyzer can provide a visual explanation for the corresponding model and prediction results.
At present, as energy resources are increasingly scarce, it is necessary to apply a highly efficient savings policy. In this context, it is essential to study and analyze electrical systems, mainly ...supply systems in production and service facilities. It is important to point out that in peak electricity hours, even the least efficient generating machines have to work, increasing oil consumption per kWh generated; on the other hand, there are great difficulties in transferring power from different generating units to the places where electricity is consumed in those critical hours. Therefore, when the energy demand is not in correspondence with the generation of the generating plants, it is then necessary to interrupt some loads, producing the well-known blackouts and, consequently, the inconvenience and affectations of the users of the electrical service, it is in these cases where the need for electric charge accommodation becomes evident. Therefore, this article aims to propose an application to accommodate loads in industrial systems, through a graphical interface, based on the free software Scilab, which among other things allows load accommodation considering the installation or not of a photovoltaic system. To verify its usefulness, the UEB Pinturas Vitral located in the municipality of San José de las Lajas, Mayabeque Province, Cuba, has 2 work shifts.
The structure and “metabolism” (movement and conversion of goods and energy) of urban areas has caused cities to be identified as “super‐organisms”, placed between ecosystems and the biosphere, in ...the hierarchy of living systems. Yet most such analogies are weak, and render the super‐organism model ineffective for sustainable development of cities. Via a cluster analysis of 15 shared traits of the hierarchical living system, we found that industrialized cities are more similar to eukaryotic cells than to multicellular organisms; enclosed systems, such as factories and greenhouses, paralleling organelles in eukaryotic cells. We further developed a “super‐cell” industrialized city model: a “eukarcity” with citynucleus (urban area) as a regulating centre, and organaras (enclosed systems, which provide the majority of goods and services) as the functional components, and cityplasm (natural ecosystems and farmlands) as the matrix. This model may improve the vitality and sustainability of cities through planning and management.
In industrial cities, the quantitative configuration, spatial distribution and functioning of organaras, citynucleus and cityplasm are mapping of organelles, nucleus and cytoplasm of eukaryotic cells. The deduction from cell to city builds a super‐cell city model and a bionic paradigm for city to provide goods and services sustainably and vitality.
Multivariate time series anomaly detection plays an important role for the safe operation of industrial devices and systems. At present, many effective methods have the major limitation that the ...changes in information propagation between variables are not considered when anomalies occur. Therefore, this article proposes a novel graph structure change-based anomaly detection on multivariate time series (GSC-MAD). First, a stable graph structure under normal conditions is obtained and a single-step prediction for all variables is achieved from a high-dimensional time-series embedding representation learned from the normal data. Then, anomaly detection is achieved by combining the variable behavior deviation reflected by prediction errors and the information propagation deviation between variables reflected by GSC. Extensive experiments on five real-world benchmarks are conducted to demonstrate the effectiveness of the proposed method and compared with current state-of-the-art (SOTA) baselines, a relative improvement of 6.64% on the average F1 is achieved. Moreover, an actual chemical industrial case is provided to verify the effect of the GSC-MAD and a relative improvement of 4.03% is achieved on the F1 metric compared with SOTA baselines. Comparison experiment results show that the proposed method achieves the SOTA results in terms of current baselines. Further experiment analysis shows the good interpretability of the proposed method for detected anomalies.
In this digital, internet-based world, it is not new to face cyber attacks from time to time. A number of heavy viruses have been made by hackers, and they have successfully given big losses to our ...systems. In the family of these viruses, the Stuxnet virus is a well-known name. Stuxnet is a very dangerous virus that probably targets the control systems of our industry. The main source of this virus can be an infected USB drive or flash drive. In this research paper, we study a mathematical model to define the dynamical structure or the effects of the Stuxnet virus on our computer systems. To study the given dynamics, we use a modified version of the Caputo-type fractional derivative, which can be used as an old Caputo derivative by fixing some slight changes, which is an advantage of this study. We demonstrate that the given fractional Caputo-type dynamical model has a unique solution using fixed point theory. We derive the solution of the proposed non-linear non-classical model with the application of a recent version of the Predictor–Corrector scheme. We analyze various graphs at different values of the arrival rate of new computers, damage rate, virus transmission rate, and natural removal rate. In the graphical interpretations, we verify the values of fractional orders and simulate 2-D and 3-D graphics to understand the dynamics clearly. The major novelty of this study is that we formulate the optimal control problem and its important consequences both theoretically and mathematically, which can be further extended graphically. The main contribution of this research work is to provide some novel results on the Stuxnet virus dynamics and explore the uses of fractional derivatives in computer science. The given methodology is effective, fully novel, and very easy to understand.
Industrial systems can be complex and not intuitive to perceive. Therefore, students in technology and engineering programs can benefit from developing mental models of industrial systems during ...their journey in college. However, more often than not, these students do not have access to industrial facilities; thus, developing mental models for systems is a challenge. This paper examines the merit of an Immersive Virtual Reality (IVR) framework application in creating proper mental models for industrial systems in technology and engineering students. Two IVR applications were developed. One IVR application afforded interaction with components of a prefabricated industrial cooling water system (CWVR). In the other application, students designed and built industrial systems with IVR (system designer VR SDVR). SDVR facilitated constructive‐generative engagement. A group of 33 students was divided into two; one group (the Design, experimental group) was tasked with building a system with SDVR and interacting with the cooling water system in CWVR. The other group was tasked with directly interacting with the CWVR without building a system with SDVR (the Interaction, comparison group). Students' mental models of the cooling water system in CWVR were evaluated following completing the interaction experience with CWVR. The results demonstrate that the causal model notion of the mental model of the cooling water system was significantly higher in the Design, experimental group. The results suggest that designing a rich IVR application that facilitates constructive‐generative engagements may carry merit in informing student mental models of complex technical concepts.
Lay Description
Industrial systems can be complex and not intuitive to perceive.
Students in technology‐related programs may struggle to develop mental models for systems.
Immersive virtual reality applications for industrial systems were developed and tested.
Results indicate that virtual reality might inform mental models of systems in students.
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