Journal of Ambient Intelligence and Smart Environments

Strongly rooted in the Internet of Things and Cyber-Physical Systems-enabled manufacturing, disruptive paradigms like the Factory of the Future and Industry 4.0 envision knowledge-intensive industrial intelligent environments where smart personalized products are created through smart processes and procedures. The 4th industrial revolution will be based on Cyber-Physical Systems that will monitor, analyze and automate business processes, transforming production and logistic processes into smart factory environments where big data capabilities, cloud services and smart predictive decision support tools are used to increase productivity and efficiency. This survey provides insights into the latest developments in these domains, and identifies relevant research challenges and opportunities to shape the future of intelligent manufacturing environments.

“Industry 4.0” is recognized as the future of industrial production in which concepts as Smart Factory and Decentralized Decision Making are fundamental. This paper proposes a novel strategy to support decentralized decision, whilst identifying opportunities and challenges of Industry 4.0 contextualizing the potential that represents industrial digitalization and how technological advances can contribute for a new perspective on manufacturing production. It is analysed a set of barriers to the full implementation of Industry 4.0 vision, identifying areas in which decision support is vital. Then, for each of the identified areas, the authors propose a strategy, characterizing it together with the level of complexity that is involved in the different processes. The strategies proposed are derived from the needs of two of Industry 4.0 main characteristics: horizontal integration and vertical integration. For each case, decision approaches are proposed concerning the type of decision required (strategic, tactical, operational and real-time). Validation results are provided together with a discussion on the main challenges that might be an obstacle for a successful decision strategy.

Augmented Reality (AR) bridges the gap between the real and the virtual world by bringing virtual information to a real environment as seamlessly as possible. The need for better perception of knowledge-intensive complex maintenance tasks and access to large amounts of documents and data makes the use of AR technology promising in a maintenance domain. Context-awareness enhances the usability of such AR applications, i.e. the output and behavior of the system will be adapted according to different contexts, such as the user location, preferences, devices, etc. to afford a higher level of personalization. The adaptation needs to be efficient in terms of performance and speed. This paper presents an optimized framework which combines context-awareness and AR for training and assisting technicians in maintaining equipment in an industrial context to improve field workers effectiveness. Ontology is used to model a maintenance context, and Semantic Web Rule Language (SWRL) provides logical reasoning. This optimized framework utilizes a behavior network to select a collection of suitable actions based on the current step of an ongoing task, and applies context-based inferred information from the ontology to each member of this collection. Evaluation results comparing the performance of the proposed framework with conventional ontology alone in a maintenance domain confirmed that the proposed framework in this research provides the same results as the ontology in terms of content, but it runs much faster in terms of run-time and performance. The proposed context-aware framework is quite valuable especially in terms of response time and performance for maintenance systems with a large number of maintenance activities.

Multi-motor drives, the typical examples of which are continuous lines for tension processing of various materials, are complex and coupled MIMO nonlinear systems, the parameters of which are difficult to identify. This paper presents the design of optimal control of a continuous production line using a fuzzy model based approach with emphasis on minimal knowledge on the controlled technology. In the first part of the paper we describe the method of the black-box fuzzy model design based only on the system’s measured input/output data without the necessity of preliminary knowledge of its internal structure and parameters. This fuzzy model is in the second part used in the design of optimal parameters of a PI controller of continuous line on basis of the chosen quadratic optimality criterion. The realized experimental measurements on a continuous line physical laboratory model confirmed the effectiveness and the good dynamic properties of the proposed optimal controller and also its applicability to MIMO nonlinear dynamic systems with as little previous knowledge as possible. In order to demonstrate its effectiveness, the performance of the designed controller is compared with that of a conventional controller and a classic fuzzy controller designed on the basis of linguistic rules.

Conflicting rules and rules with exceptions are very common in natural language specification employed to describe the behaviour of devices operating in a real-world context. This is common exactly because those specifications are processed by humans, and humans apply common sense and strategic reasoning about those rules to resolve the conflicts. In this paper, we deal with the challenge of providing, step by step, a model of energy saving rule specification and processing methods that are used to reduce the consumptions of a system of devices, by preventing energy waste. We argue that a very promising non-monotonic approach to such a problem can lie upon Defeasible Logic, following therefore an approach that has shown success in the current literature about usage of this logic for conflict rule resolution and for human–computer interaction in complex systems. Starting with rules specified at an abstract level, but compatibly with the natural aspects of such a specification (including temporal and power absorption constraints), we provide a formalism that generates the extension of a basic Defeasible Logic, which corresponds to turned on or off devices.

Simulation modelling has an ever increasing importance for complex systems. Manufacturing and related material flow or logistic systems are typical fields of application. Latest trends such as Cyber-physical systems and Industry 4.0 give a significant boost to simulation modelling as these require a digital model of the system. Complex manufacturing and related material flow systems are subject to frequent changes and pose a Big Data problem, which raises stronger requirements regarding self-adaptiveness. Conventional simulation models are to be adapted only via user interaction. Previous research steps have concentrated on the establishment of a novel simulation model structure, the so called “Jellyfish” model which unifies layout and process-type simulation models. Visualization of both aspects simultaneously enables interacting users to better understand the systems’ operation compared to the conventional models. The current paper focuses on the adaptive capability of the new model. We have concentrated on the hardest type of adaptation, the structural adaptation. In this paper, an ontology-driven component based approach is presented and explained further through an example. Application of automated ontology-matching in simulation environment is a novel approach enabling the simulation model to adapt its structure without the necessity of manual interaction.