The Internet of Things (IoT) has become an enabler paradigm for different applications, such as healthcare, education, agriculture, smart homes, and recently, enterprise systems (E-IoTs). Significant advances in IoT networks have been hindered by security vulnerabilities and threats, which, if not addressed, can negatively impact the deployment and operation of IoT-enabled systems. This study addresses IoT security and presents an intelligent two-layer intrusion detection system for IoT. The system's intelligence is driven by machine learning techniques for intrusion detection, with the two-layer architecture handling flow-based and packet-based features. By selecting significant features, the time overhead is minimized without affecting detection accuracy. The uniqueness and novelty of the proposed system emerge from combining machine learning and selection modules for flow-based and packet-based features. The proposed intrusion detection works at the network layer, and hence, it is device and application transparent. In our experiments, the proposed system had an accuracy of 99.15% for packet-based features with a testing time of 0.357 μs. The flow-based classifier had an accuracy of 99.66% with a testing time of 0.410 μs. A comparison demonstrated that the proposed system outperformed other methods described in the literature. Thus, it is an accurate and lightweight tool for detecting intrusions in IoT systems.
Dynamic reconfigurability and adaptability are crucial features of the future manufacturing systems that must be supported by adequate software technologies. Currently, they are typically achieved as add-ons to existing software tools and run-time systems, which are not based on any formal foundation such as formal model of computation (MoC). This paper presents the new programming paradigm of Service Oriented SystemJ (SOSJ), which targets dynamic distributed software systems suited for future manufacturing applications. SOSJ is built on a merger and the synergies of two programming concepts of (1) Service Oriented Architecture (SOA), to support dynamic software system composition, and (2) SystemJ programming language based on a formal MoC, which targets correct by construction design of static distributed software systems. The resulting programming paradigm allows the design and implementation of dynamic distributed software systems.
Closed-loop model checking, a formal verification technique for industrial automation systems, increases the richness of specifications to be checked and reduces the state space to be verified compared to the open-loop case. To be applied, it needs the controller and the plant formal models to be coupled. There are approaches for controller synthesis, but little has been done regarding plant model construction. While manual plant modeling is time consuming and error-prone, discretizing a simulation model of the plant leads to state excess. This paper aims to solve the problem of automatic plant model construction from existing specification, which is represented in the form of plant behavior examples, or traces, and temporal properties. The proposed method, which is based on the translation of the problem to the Boolean satisfiability problem, is evaluated and shown to be applicable on several case study plant model synthesis tasks and on randomly generated problem instances.
The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.
Nowadays an increasing number of industries are considering moving towards being Industry 4.0 compliant. But this transition is not straightforward: transfer to new system can lead to significant production downtime, resulting in delays and cost overruns. The best way is systematic seamless transition to newer and advanced technologies that Industry 4.0 offers. This paper proposes a framework based on automatic synthesis methods that learns the behavior of an existing legacy programmable logic controller (PLC) and generates state machines that can be incorporated into IEC 61499 function blocks. Proposed algorithms are based on Boolean satisfiability (SAT) solvers. The first algorithm accepts a set of noisy PLC traces and produces a set of candidate state machines that satisfy the traces. The second algorithm accepts error-free traces and synthesizes a modular controller that may be distributed across several physical devices. The toolchain architecture is exemplified on a laboratory scale Festo mechatronic system.
An approach for automatic reconstruction of automation logic from execution scenarios using a metaheuristic algorithm is proposed. IEC 61499 basic function blocks are chosen as implementation language and reconstruction of Execution Control Charts for basic function blocks is addressed. The synthesis method is based on a metaheuristic algorithm that combines ideas from ant colony optimization and evolutionary computation. Execution scenarios can be recorded from testing legacy software solutions. At this stage results are only limited to generation of basic function blocks having only Boolean input/output variables.
We propose a two-stage exact approach for identifying finite-state models of function blocks based on given execution traces. First, a base finite-state model is inferred with a method based on translation to the Boolean satisfiability problem, and then, the base model is generalized by inferring minimal guard conditions of the state machine with a method based on translation to the constraint satisfaction problem.
An increasingly important goal of industrial automation systems is to continuously optimize physical resource utilization such as materials. Distributed automation is seen as one enabling technology for achieving this goal, in which networking controller nodes collaborate in a peer to peer way to form a new paradigm, namely industrial cyber-physical systems (iCPS). In order to achieve rapid response to changes from both high level control systems and plant environment, the proposed self-manageable agent relies on the use of the Service-Oriented Architecture (SOA) that improves flexibility and interoperability. It is enhanced by the autonomic service management (ASM) to implement software modifications in a fully automatic manner, thus achieving self-manageable and adaptive industrial cyberphysical systems. The architecture design of the autonomic service manager is provided and integration with SOA based execution environment is illustrated. Preliminary tests on selfmanagement are completed using a case study of airport baggage handling system.
The IEC 61499 standard is designed for distributed control and proposes new visual form of programming using block diagrams with embedded state machines and unlimited hierarchical nesting and distribution across networking devices. Such visual programs require new methods of automatic syntactic and semantic analysis. This paper proposes a new approach to semantic analysis using multiple-layered ontological knowledge representation and rule-based inference engine. Its working is demonstrated on example.
Downtime is a key performance index for industrial automation systems. An industrial automation system achieves maximum productivity when its downtime is reduced to the minimum. One approach to minimize downtime is to predict system faults and recover from them automatically. A cloud-based decision support system is proposed for rapid problem identifications and to assist the self-management processes. By running multiple parallel simulations of control software with real-time inputs ahead of system time, faults could be detected and corrected automatically using autonomous industrial software agents. Fault trees, as well as control algorithms, are modeled using IEC 61499 function blocks that can be directly executed on both physical controllers and cloud services. A case study of water heating process is used to demonstrate the self-healing process supported by the cloud-based decision support system.
In recent years, requirements for interoperability, flexibility, and reconfigurability of complex automation industry applications have increased dramatically. The adoption of service-oriented architectures (SOAs) could be a feasible solution to meet these challenges. The IEC 61499 standard defines a set of management commands, which provides the capability of dynamic reconfiguration without affecting normal operation. In this paper, a formal model is proposed for the application of SOAs in the distributed automation domain in order to achieve flexible automation systems. Practical scenarios of applying SOA in industrial automation are discussed. In order to support the SOA IEC 61499 model, a service-based execution environment architecture is proposed. One main characteristic of flexibility-dynamic reconfiguration-is also demonstrated using a case study example.
Service-oriented architecture is increasingly applied in industrial cyber-physical systems to provide better flexibility and interoperability between various systems and devices. In the previous work (IEEE Trans. On Industrial Informatics, Vol. 11, No. 3, pp 771 – 781, June 2015), a service-based execution environment for IEC 61499 is proposed to enhance flexibility and interoperability in distributed automation systems. In this paper we discuss questions raised by some readers, for example performance overhead of the proposed method. Also other clarifications are made in order to address possible confusions expressed in the received comments
The emergence of artificial intelligence (AI), empowered by robust computing infrastructure and abundance of data, maintains potential for radical transformation of human society, essentially a third phase in evolution. Numerous research endeavor, policy development, and thought-leadership are presently in progress aimed at discovering data-driven intelligent decision-making solutions for smart cities, smart grids, smart homes, and informed citizens as well as addressing potential risks posed by AI workplace automation. Joining this broad effort, this Special Section contributes six research articles that consolidate recent developments in AI for industrial informatics.
The accurately estimated state is of great importance for maintaining a stable running condition of power systems. To maintain the accuracy of the estimated state, bad data detection (BDD) is utilized by power systems to get rid of erroneous measurements due to meter failures or outside attacks. However, false data injection (FDI) attacks, as recently revealed, can circumvent BDD and insert any bias into the value of the estimated state. Continuous works on constructing and/or protecting power systems from such attacks have been done in recent years. This survey comprehensively overviews three major aspects: constructing FDI attacks; impacts of FDI attacks on electricity market; and defending against FDI attacks. Specifically, we first explore the problem of constructing FDI attacks, and further show their associated impacts on electricity market operations, from the adversary's point of view. Then, from the perspective of the system operator, we present countermeasures against FDI attacks. We also outline the future research directions and potential challenges based on the above overview, in the context of FDI attacks, impacts, and defense.
Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and operational changes must be detected and managed, triggering rapid model adaptation mechanisms. This article presents an adaptive learning approach based on a dendritic cell algorithm for drift detection and a deep neural network model that dynamically adapts to new operational conditions. A kernel density estimator with drift-based bandwidth is used to generate synthetic data for a faster adaptation, focusing on fine-tuning the lowest neural layers. Experimental results over a real-world industrial problem shed light on the outperforming behavior of the proposed approach when compared to other drift detectors and classification models.
Order scheduling is of vital importance in discrete manufacturing industries. This paper takes fashion industry as an example and discusses the robust order scheduling problem in the fashion industry. In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as preproduction events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this paper, by considering the preproduction events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multiobjective evolutionary algorithm called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider preproduction events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.
The international standard IEC 61499 for the design of distributed industrial control systems defines an abstract model of function blocks (FB) which allows many different semantic interpretations. As a consequence, in addition, so-called execution models were proposed to specify the execution order of FBs. The variety of models leads to the incompatibility of tools and hinders the portability of automation software. To achieve a degree of execution model independence, in this paper, design patterns are suggested that make FB systems-robust to changes of execution semantics. A semantic-robust pattern is defined for a particular source execution model. The patterns themselves are implemented by means of the FB apparatus and therefore are fairly universal. The patterns can be defined and implemented using the FB transformations expressed in terms of Attributed Graph Grammars.
This paper assesses the communication, information and functional requirements of Virtual Power Plants (VPPs). A conceptual formulation of the interoperability requirements is presented as well as a comparative study of their fulfillment by state-of-the-art communication techniques. VPP requirements are then mapped against services and information models of IEC 61850 and CIM power utility automation standards. Proposals are given for extensions of the IEC 61850 standard to enhance the interaction between VPP controller and the distributed energy resources. Finally the methodology and concepts are applied to a specific VPP consisting of hydro and wind plants, solar PV and storage facilities. Several applications to provide grid services from the proposed VPP in an existing 50 kV grid are covered. The implementation of the VPP communication and control architecture in the SCADA of demonstration plant is also presented.
In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three 'problem transformation' methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation
The uncertainty caused by the variability in renewable energy production requires the engagement of consumer-side energy production and consumption to provide sufficient flexibility and reliability for the power grid. This study presents an algorithm for allocating tasks to distributed energy resources allowing consumers to provide flexibility for frequency containment reserves. The task allocation algorithm aims at supporting the plug and play of energy resources, and it avoids the need for hard real-time messages during the coordination of the resources. The algorithm combines a novel control strategy with an information and communication technology architecture. The main decision logic of the algorithm is defined together with the distributed control logic. A prototype implementation of the overall system for frequency control is used to evaluate the performance of the algorithm. The simulation results show that the algorithm achieves the specified objectives, and has advantages compared to the state of the art solution.
The rapid development of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) brings new security threats by exposing secret and private data. Thus, information security has become a major concern in the communication environment of IIoT and AI, where security and privacy must be ensured for the messages between a sender and the intended recipient. To this end, we propose a method called HHO-IWT for covert communication and secure data in the IIoT environment based on digital image steganography. The method embeds secret data in the cover images using a metaheuristic optimization algorithm called Harris hawks optimization (HHO) to efficiently select image pixels that can be used to hide bits of secret data within integer wavelet transforms. The HHO-based pixel selection operation uses an objective function evaluation depending on two phases: exploitation and exploration. The objective function is employed to determine an optimal encoding vector to transform secret data into an encoded form generated by the HHO algorithm. Several experiments are conducted to validate the performance of the proposed method with respect to visual quality, payload capacity, and security against attacks. The obtained results reveal that the HHO-IWT method achieves higher levels of security than the state-of-the-art methods and that it resists various forms of steganalysis. Thus, utilizing this approach can keep unauthorized individuals away from the transmitted information and solve some security challenges in the IIoT.
Secure real-time data about goods in transit in supply chains needs bandwidth having capacity that is not fulfilled with the current infrastructure. Hence, 5G-enabled Internet of Things (IoT) in mobile edge computing is intended to substantially increase this capacity. To deal with this issue, we design a new efficient lightweight blockchain-enabled RFID-based authentication protocol for supply chains in 5G mobile edge computing environment, called LBRAPS. LBRAPS is based on bitwise exclusive-or (XOR), one-way cryptographic hash and bitwise rotation operations only. LBRAPS is shown to be secure against various attacks. Moreover, the simulation-based formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool assures that LBRAPS is secure. Finally, it is shown that LBRAPS has better trade-off among its security and functionality features, communication and computation costs as compared to those for existing protocols.
One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Many of them rely on the qualitative interpretation of the results, a fact that requires significant user expertise, and that makes their implementation in portable condition monitoring devices difficult. In this paper, we present an automated method for the detection of the number of broken bars of an induction motor. The method is based on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic component that emerges once a rotor bar is broken. After the isolation of this component, a number of stages are applied that transform the continuous-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a representation that can be easily manipulated by a nearest neighbor classifier. The approach is tested using simulation as well as experimental data, achieving high-classification accuracy.
A large number of potential applications for Wireless Sensor and Actuator Networks (WSAN) have yet to be embraced by industry despite high interest amongst academic researchers. This is due to various factors such as unpredictable costs related to development, deployment and maintenance of WSAN, especially when integration with existing IT infrastructure and legacy systems is needed. Service-Oriented Architecture (SOA) is seen as a promising technique to bridge the gap between sensor nodes and enterprise applications such as factory monitoring, control and tracking systems where sensor data is used. To date, research efforts have focused on middleware software systems located in gateway devices that implement standard service technology, such as Devices Profile for Web Services (DPWS), for interacting with the sensor network. This paper takes a different approach - deploying interoperable Simple Object Access Protocol (SOAP)-based web services directly on the nodes and not using gateways. This strategy provides for easy integration with legacy IT systems and supports heterogeneity at the lowest level. Two-fold analysis of the related overhead, which is the main challenge of this solution, is performed; Quantification of resource consumption as well as techniques to mitigate it are presented, along with latency measurements showing the impact of different parts of the system on system performance. A proof-of-concept application using Mulle - a resource-constrained sensor platform - is also presented.
Owing to the semantic ambiguities, it has hindered the promotion of IEC 61499 in the field of industrial automation. In order to solve the thorny problem, this paper proposes an implementation scheme for performing formal modelling and simulation verification of semantics of functional block networks. Based on the synchrony hypothesis, the formal execution model is defined according to the fixed point semantics assuming that the behavior of a component functional block is monotonic. Subsequently, through specifying the evaluation of function blocks as a process of solving the least fixed point problem and transforming the network topology into a directed graph, a connectivity attenuation-based algorithm is put forward to ascertain the optimal scheduling policy of function blocks with the minimum overhead. Finally, by conducting the experiment for an industrial application, the feasibility and validity of the presented implementation scheme is proved.
The IEC 61499 standard provides means to specify distributed control systems in terms of function blocks. For the deployment, each device may hold one or many logical resources, each consisting of a function block network with service interface blocks at the edges. The execution model is event driven (asynchronous), where triggering events may be associated with data (and seen as messages). In this paper, we propose a low complexity implementation technique allowing to assess end-to-end response times of event chains spanning over a set of networked devices. Based on a translation of IEC 61499 to RTFM1-tasks and resources, the response time for each task in the system at device-level can be derived using established scheduling techniques. In this paper, we develop a holistic method to provide safe end-to-end response times taking both intra- and inter-device delivery delays into account. The novelty of our approach is the accuracy of the system scheduling overhead characterization. While the device-level (RTFM) scheduling overhead was discussed in previous works, the network-level scheduling overhead for switched Ethernets is discussed in this paper. The approach is generally applicable to a wide range of COTS Ethernet switches without a need for expensive custom solutions to provide hard real-time performance. A behavior characterization of the utilized switch determines the guaranteed response times. As a use case, we study the implementation onto (single-core) ARMcortex based devices communicating over a switched Ethernet network. For the analysis, we define a generic switch model and an experimental setup allowing us to study the impact of network topology as well as 802.1Q quality of service in a mixed critical setting. Our results indicate that safe sub millisecond end-to-end response times can be obtained using the proposed approach.
As a novel network infrastructure that realizes the interconnection of humans, machines, and things, the 5G-based or blockchain-based applications have been widely deployed in the Industrial Internet of Things (IIoT). However, there are still many challenges to be solved, such as poor scalability, low efficiency, and privacy leakages. In the special issue, we present eight advanced solutions in data analysis, secure communication, authentication, access control, and data deduplication.
Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions are designed to perform a single or minimal number of tasks (primary usage). We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data include our behaviors, habits, preferences, life patterns, and resource consumption. To discover such knowledge, we need to acquire and analyze this data together in a large scale. To discover useful information and deriving conclusions toward supporting efficient and effective decision making, industrial IoT platform needs to support variety of different data analytics processes such as inspecting, cleaning, transforming, and modeling data, especially in big data context. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general-purpose industrial-grade big data analytics platforms need to address many challenges. We have accepted six manuscripts out of 24 submissions for this special section (25% acceptance rate) after the strict peerreview processes. Each manuscript has been blindly reviewed by at least three external reviewers before the decisions were made. The papers are briefly summarized.
The papers in this special section focus on information technology in industrial automation applications. Information technologies play a crucial role in the current and future developments of industrial automation. There are numerous strategic agendas on future manufacturing that have appeared recently worldwide and all of them emphasize the role of information technologies in automation in shaping up the future of production industries. For example, according to the German development agenda Industrie 4.0, the main driving force of the new industrial revolution is the Internet of things (IoT) and Cyber-Physical Systems (CPS). The IoT concept is becoming a major driver for many industrial applications. In manufacturing, it leads to flattening of the control pyramid, thus, increasing flexibility and enabling unprecedented level of production flexibility and adaptability, making it possible and feasible to produce products in smaller amounts, with shorter time to markets and higher economic efficiency. In the manufacturing environment, CPS comprise smart machines, storage systems, and production facilities capable of autonomously exchanging information, triggering actions, and controlling each other independently. The research community effort has been focusing on systems interoperability, performance, and efficiency of the design process, as well as assurance in the correctness of systems behavior.
This paper presents the use of automated machine learning for solving a practical problem of a real-life Smart Waste Management system. In particular, the focus of the paper is on the problem of detection (i.e., binary classification) of emptying of a recycling container using sensor measurements. Numerous data-driven methods for solving the problem are investigated in a realistic setting where most of the events are not actual emptying. The investigated methods include the existing manually engineered model and its modification as well as conventional machines learning algorithms. The use of machine learning allows improving the classification accuracy and recall of the existing manually engineered model from $86.8\%$ and $47.9\%$ to $99.1\%$ and $98.2\%$ , respectively, when using the best performing solution. This solution uses a Random Forest classifier on a set of features based on the filling level at different given time spans. Finally, compared to the baseline existing manually engineered model, the best performing solution also improves the quality of forecasts for emptying time of recycling containers.
Industrial automation systems need to be highly dependable; they should not merely function as expected but also do so in a reliable, safe and secure manner. Formal methods are mathematical techniques used to describe computer systems, both hardware, and software. Formal methods can greatly aid in developing dependable systems and can be used across all phases of the system development lifecycle, right from customer requirement gathering through design and implementation, verification and validation (testing), maintenance and even documentation. This state-of-the-art survey reports existing formal approaches for creating more dependable industrial automation systems, focussing on static or offline, as opposed to runtime or online, formal methods. This article categorises existing works as per the requirements engineering, design and implementation, and testing phases of the system development life cycle, allowing us to identify gaps in current research and promising future directions for each of these phases.
This paper proposes a new software composition method for automated machines that exploits their mechatronic modularity. It is demonstrated that desired behavior of a certain class of machines can be composed of behaviors of its mechatronic components, including fully decentralized scheduling and operation control. This aims at increased performance of software design and maintenance, as well as systems' flexibility and reconfigurability. The IEC 61499 Function Blocks' (FBs) architecture is used as an implementation platform that enables system-level simulation and transparency of deployment. A configurable pick-and-place (PnP) manipulator with decentralized control synthesized using the proposed approach is chosen as an illustrative example
The intention of this paper is to provide an overview of using agent and service-oriented technologies in intelligent energy systems. It focuses mainly on ongoing research and development activities related to smart grids. Key challenges as a result of the massive deployment of distributed energy resources are discussed, such as aggregation, supply-demand balancing, electricity markets, as well as fault handling and diagnostics. Concepts and technologies like multiagent systems or service-oriented architectures are able to deal with future requirements supporting a flexible, intelligent, and active power grid management. This work monitors major achievements in the field and provides a brief overview of large-scale smart grid projects using agent and service-oriented principles. In addition, future trends in the digitalization of power grids are discussed covering the deployment of resource constrained devices and appropriate communication protocols. The employment of ontologies ensuring semantic interoperability as well as the improvement of security issues related to smart grids is also discussed.
Valeriy Vyatkin, Guest Editor, IEEE Transactions on Engineering Management, talks about a Special Section in the November 2013 issue on software engineering in industrial automation. This Special Section is opened by paper that proposes a novel approach for controller synthesis on shop-floor level for discrete-event systems. The purpose of design is to create dependable software with properties that can be guaranteed by design. Another paper presents a method to develop and implement real-time capable industrial automation software that increases the dependability of production automation systems by means of soft sensors. One more paper presents a Model- Driven Engineering approach, which combines the Unified Modeling Language (UML) and Aspect-Oriented Software Development (AOSD) to design real-time and embedded automation systems. The Special Section is concluded with paper that represents a practical application of several advanced software engineering techniques in the exciting application area of thermonuclear physics
This review paper discusses the industrial and research activities around the IEC 61499 architecture for distributed automation systems. IEC 61499 has been developed to enable intelligent automation where the intelligence is genuinely decentralized and embedded into software components, which can be freely distributed across networked devices. With the recent emergence of professionally made software tools and dozens of hardware platforms, IEC 61499 is getting recognition in industry. This paper reviews research results related to the design of distributed automation systems with IEC 61499, the supporting tools and the aspects related to the execution of IEC 61499 on embedded devices. The promising application areas of IEC 61499 include flexible material handling systems, in particular airport baggage handling, flexible reconfigurable manufacturing automation, intelligent power distribution networks and SmartGrid, as well as the wide range of embedded networked systems
This paper presents one perspective on recent developments related to software engineering in the industrial automation sector that spans from manufacturing factory automation to process control systems and energy automation systems. The survey's methodology is based on the classic SWEBOK reference document that comprehensively defines the taxonomy of software engineering domain. This is mixed with classic automation artefacts, such as the set of the most influential international standards and dominating industrial practices. The survey focuses mainly on research publications which are believed to be representative of advanced industrial practices as well.
Open kNowledge Economy in Intelligent inDustrial Automation (OOONEIDA) is a new initiative for enabling decentralized, reconfigurable industrial control and automation in discrete manufacturing and continuous process systems. The goal of the OOONEIDA project is the creation of the technological infrastructure for a new, open knowledge economy for automation components and automated industrial products. This will be done by further development of the concept of reusable portable software modules (function blocks) and by their application in the time- and cost-effective specification, design, validation, realization, and deployment of intelligent mechatronic components in distributed industrial automation and control systems.
This paper deals with refactoring of execution control charts of IEC 61499 basic function blocks as a means to improve the engineering support potential of the standard in development of industrial control applications. The main purpose of the refactoring is removal of arcs without event inputs. Extended refactoring, proposed in this paper, also helps to get rid of potential deadlock states. The ECC refactoring is implemented as a set of graph transformation rules. A prototype has been implemented using the AGG software tool. The refactoring can help in implementing equivalent transformation of control programs without introducing errors.
Industry 4.0 has become more popular due to recent developments in Cyber-Physical Systems (CPS), big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications such as product lifecycle management are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the off-line prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally-used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.
This paper presents a novel model-driven software architecture for systems with high degree of redundancy and modularity of the equipment. The architecture is based on totally decentralized control. It combines adaptability and robustness of multi-agent control architectures with portability and interoperability benefits of IEC 61499 function block architecture. The architecture has been successfully proven feasible on a number of field trials, including modeling and implementation of a medium-scale airport baggage handling control. Deployment was done on distributed networks consisting of configurations ranging from a few to dozens of communicating control nodes. The work confirmed the ability to deliver similar functional characteristics as centralized systems but in a distributed implementation. Performance testing and development verified sufficient performance and software life-cycle benefits
This paper proposes a method for the automatic generation of smart-grid automation systems software from specifications that includes physical system layout and domain-specific functional “recipes.” The generated software has component organization and implements a decentralized approach to smart grid control, which reduces complexity of automation systems design and modification. The proposed method relies on synergies of two industrial standards IEC 61850 and IEC 61499, which are used to represent a part of the specification and the resulting software model, respectively. Then, the specification model is created in a form of ontology and the generation is based on ontology transformation. The proposed method requires an extension to the Semantic Web Rule Language (SWRL) to support the change of an ontology axioms set for the purpose of ontology transformation. The proposed transformation language, called extended SWRL, is introduced and illustrated in use. The result shows the viability of eSWRL as an ontology transformation language when demonstrated in a power distribution case study system.
Smart grid is a Cyber-Physical System with a high level of complexity due to its de-centralized infrastructure. IEC 61850 and IEC 61499 are two industrial standards which can address the challenges introduced by the Smart Grid on the substation automation level. Development of Smart Grid automation software is very time consuming process due to the need to address many requirements and high degree of customisation in every new substation. This limits the adoption of such smart grid technologies as digital substation. This paper aims at addressing this limitation by applying a semi-formal boilerplates model of functional requirements originally presented in informal natural language. The boilerplates are then modelled formally in an ontology for MDE model transformation. The contribution of this paper is the development of the semi-formal and formal boilerplate representation in the form of ontology to formulate Smart Grid requirements and demonstrating how functional requirements can be translated to IEC 61499 control codes using MDE to auto-generate an IEC 61499 PAC control system with structure and control flow. The MDE framework augmented with the requirements models is illustrated on a case study from CIGRE representing different stages of modelling in the proposed framework.
This paper proposes a co-simulation environment for “hardware in the loop” or “software in the loop” validation of distributed controls in Smart Grid. The controls are designed using model-driven engineering with the IEC 61499 Function Block architecture. These are connected with plant models, for example in MATLAB/Simulink, through communication channels such as UDP or TCP sockets. This solution enables multi-closed-loop plant-controller simulation. The communication between plant and controller is event-driven. In order to perform realistic simulation, the proposed solution takes into account computation and communication delays on the controller side in Function Blocks and compensates model time on the plant side in MATLAB model accordingly. Causality and accuracy of the method have been formally addressed. This approach has been tested and demonstrated with several Smart Grid-related examples.
This paper is concerned with the consensus of multiple Euler-Lagrange systems with pulse-width modulated sampled-data control. Different from traditional sampled-data strategies, a pulse-modulated sampled-data strategy is developed to realize the consensus of multiple Euler-lagrange systems, in which a pulse function that can be distinct at different sampling instants is proposed to modulate the sampling interval. In addition, a new definition of average sampling interval, which is parallel to the average dwell time in switching control or average impulsive interval in impulsive control, is proposed to characterize the number of the updating of the sampling controller during some certain interval. The proposed average sampling interval makes our sampled-data strategy more suitable for a wide range of sampling signals. By utilizing the comparison principle, a sufficient criterion is obtained to guarantee the consensus of multiple Euler-Lagrange systems. The sufficient criterion is heavily dependent on the actual control duration time, the upper and lower bounds of the pulse function and the communication graph. Finally, a simulation example is presented to verify the applicability of the proposed results.