Esteban Arroyo

Evolution of production facilities and its impact on non-functional requirements

Due to high acquisition costs, production facilities are to operate for many years or even decades to be profitable. During operation, application and customer requirements change rather frequently. Therefore, a process operator must constantly evolve the control software and the underlying system. This task is restricted by specific constraints in the domain of production systems (e.g. short reaction times, high dependency on physics, etc.) hindering the proper use of formal engineering processes, which results in a lack of explicit documentation. Under such circumstances, it is evident that long-living automation software systems require special strategies to deal with incomplete information. Moreover, due to the complexity of production plants, the interconnection between evolution scenarios and system requirements might be complex. Then, a link between evolution and fulfillment of requirements is to be defined. In an effort to give a structured overview of the resulting difficulties due to improperly performed evolution steps in production facilities, this contribution presents a categorization of evolution scenarios from a practical point of view. In addition, interrelations between physical process measurements and high-level requirements are shown. This paper aims at describing the occurring difficulties within evolving production systems from a practical point of view and establishing a first step towards exploiting process measurements for requirement-aware production systems.

Integrating plant and process information as a basis for automated plant diagnosis tasks

Effective use of integrated process and plant knowledge may significantly increase the accuracy and reliability of automated plant diagnostics. State-of-the-art diagnosis methods, however, do not exploit the whole richness of information found in process facilities mainly due to the difficulties entailed for its collection and timely retrieval. In an effort to contribute towards the exploitation of such knowledge, this paper presents concepts to classify, integrate, and facilitate the access to relevant data as a basis for automated plant diagnosis tasks. The proposed integration is based on the Formalized Process Description Guideline VDI/VDE 3682 and comprises four fundamental information sources, namely plant connectivity, plant dimension specific-, plant component specific-, and process specific-knowledge. The resulting model is described in the data format CAEX/AutomationML, which allows for seamless and effective information exchange among different diagnostic tools. Further concepts for data access and visualization are presented in this contribution.

Derivation of Diagnostic Models Based on Formalized Process Knowledge

Abstract Industrial systems are vulnerable to faults. Early and accurate detection and diagnosis in production systems can minimize down-time, increase the safety of the plant operation, and reduce manufacturing costs. Knowledge- and model-based approaches to automated fault detection and diagnosis have been demonstrated to be suitable for fault cause analysis within a broad range of industrial processes and research case studies. However, the implementation of these methods demands a complex and error-prone development phase, especially due to the extensive efforts required during the derivation of models and their respective validation. In an effort to reduce such modeling complexity, this paper presents a structured causal modeling approach to supporting the derivation of diagnostic models based on formalized process knowledge. The method described herein exploits the Formalized Process Description Guideline VDI/VDE 3682 to establish causal relations among key-process variables, develops an extension of the Signed Digraph model combined with the use of fuzzy set theory to allow more accurate causality descriptions, and proposes a representation of the resulting diagnostic model in CAEX/AutomationML targeting dynamic data access, portability, and seamless information exchange.

Learning material flow models for manufacturing plants from data traces

Models describing the material flow of discrete manufacturing systems are important documentation artefacts and the basis for a comprehensive understanding of the underlying processes. The analysis of such models allows deriving important key performance indicators enabling the assessment of the current system implementation. However, manual modeling as well as up-to-date model maintenance is an error-prone and costly task. In an effort to allow for the automatic derivation of material flow models, this paper introduces the concept of Material Flow Petri Nets (MFPNs) and presents a learning algorithm for their automatic generation based on recorded PLC I/O data. The proposed algorithm has been evaluated on a case study of a laboratory plant with successful results.

Automatic detection and recognition of structural and connectivity objects in SVG-coded engineering documents

Integrating legacy plant and process information into engineering, control, and enterprise systems may significantly increase the efficiency of managerial and technical operations in industrial facilities. The first step towards the pursued data integration is the extraction of relevant information from existing engineering documents, many of which are stored in vector-graphics-compatible formats such as PDF. Accordingly, this paper is aimed at proposing a novel methodology for the automatic extraction of structural and connectivity information from vector-graphics-coded engineering documents. A case study of a piping and instrumentation diagram (P&ID) demonstrates the reliable performance of the approach for the recognition of symbols, annotations, and underlying connectivity.

First steps from a traffic node to traffic networks — Modeling and stability

Introducing decentralized control concepts in traffic networks opens up the potential to deal with high system complexity and to increase disturbance robustness. However, influencing the traffic dynamics with a decentralized concept, and hence having only a local view of the network, can cause undesirable effects such as oscillating traffic flows and traffic jams. This paper presents a traffic oscillation problem based on delayed feedback information. Realistic traffic data is used to generate an aggregated continuous model of a traffic intersection, based on which frequency and time domain approaches to stability analysis can be applied. Drawing upon the results of single traffic intersections, different traffic network constellations are examined to form a basis for modeling and stability analysis of complex traffic systems.

Automatische Analyse und Erkennung graphischer Inhalte von SVG-basierten Engineering-Dokumenten

Zusammenfassung Graphische Engineering-Dokumente beinhalten substanzielle Informationen für diverse Planungs- sowie Betriebsaktivitäten in der verfahrenstechnischen Industrie. Auch wenn diese Dokumente seit vielen Jahren mit dem Computer erstellt werden, werden sie in vielen Fällen jedoch als Papierausdruck oder in primitiven digitalen Formaten gespeichert, wodurch eine rechnergestützte und effiziente Auswertung dieser Dokumente erschwert wird. Um dieses Problem zu bewältigen, präsentiert dieser Beitrag eine neue Methodik für die automatische Analyse und computer-interpretierbare Beschreibung von Engineering-Dokumenten, welche als Scalable Vector Graphics (SVG) vorliegen.

Knowledge-based selection of principle solutions for sensors and actuators based on standardized plant description and semantic concepts

Specifying and selecting actuation and instrumentation devices under the analysis of multiple requirements is a complex and labor-intensive step within the engineering of process plants. In industrial practice, this task is usually conducted following a product-oriented approach, i.e. devices are selected based on vendor-specific product catalogues and typically considering only known or previously used solutions. In many cases, selection decisions are taken in a one-to-one basis, i.e. one device at a time, and resulting selection data must be entered manually in planning and enterprise software tools. Such rudimentary practices often result in suboptimal design solutions and have a direct impact on the efficiency of the engineering workflow. In an effort to overcome such limitations, this contribution presents a concept based on plant description and semantic models (resp. AutomationML and eCl@ss) which allows for the knowledge-based selection of principle solutions, i.e., abstract design alternatives fulfilling required process functions (e.g. flow measurement). This concept does not only enable a function-oriented selection process capable of considering a wider solution space but also allows the seamless integration of this procedure into the plant engineering workflow.