A Modular Architecture for the Design of Condition Monitoring Processes Available online at www sciencedirect com 2212 8271 © 2016 The Authors Published by Elsevier B V This is an open access article[.]
Available online at www.sciencedirect.com ScienceDirect Procedia CIRP 57 (2016) 410 – 415 49th CIRP Conference on Manufacturing Systems (CIRP-CMS 2016) A Modular Architecture for the Design of Condition Monitoring Processes Hans Fleischmanna,*, Johannes Kohla, Jörg Frankea a Institute for Factory Automation and Production Systems, Egerlandstr 7-9, D-91058 Erlangen, Germany * Corresponding author Tel.: +49-9131-85-28783; fax: +49-9131-85-302528 E-mail address: Hans.Fleischmann@faps.fau.de Abstract The increasing complexity of production plants in the context of Industry 4.0 poses new challenges with regards to technical maintenance and process control In this context, Cyber-Physical Systems (CPS) for intelligent condition monitoring enable fault-tolerant, predictable production systems Unfortunately, CPS and Condition Monitoring System development is challenging due to the distribution of computational tasks among heterogeneous industrial IT-architectures It is usually started from scratch, which is time-consuming and error-prone In times of CPS the employee as maintenance worker, who ensures the availability of production machinery, is still important In order to address these challenges, this publication we propose a modular architecture for Socio-CPS-based condition and process monitoring as well as fault diagnostics ©©2016 Authors Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license 2015The The Authors Published by Elsevier B.V (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of Scientific committee of the 49th CIRP Conference on Manufacturing Systems (CIRP-CMS 2016) Peer-review under responsibility of the scientific committee of the 49th CIRP Conference on Manufacturing Systems Keywords: Condition Monitoring Systems; Industry 4.0; Technical Diagnosis; Socio-Cyber-Physical Systems Introduction The ongoing industrial revolution, called Industry 4.0, is entirely transforming traditional manufacturing industries As a technical basis, Cyber-Physical Systems (CPS) as well as the expanding Internet of Things and Services (IoTS) have immensely affected value creation, business models and the organization of work in the domain of industrial production [1] CPS provide a connection between the virtual and the physical world combined with various self-x capabilities These allow the realization of a smart factory, characterized by high mutability and ergonomic working conditions [2] Along with a high level of automation, the development of intelligent monitoring and autonomous decision-making processes is important in order to control and optimize both industrial companies and entire value-adding networks efficiently In today’s factories, operation, monitoring, diagnosing and troubleshooting automated production systems is already being supported by information technology, such as Enterprise Resource Planning (ERP) Systems, Manufacturing Execution Systems (MES), Programmable Logic Controllers (PLC) and remote maintenance concepts However, functions of centralized industrial systems are being increasingly shifted to decentralized CPSs linked in an IoTS As shown in Fig 1, an expanding IoTS leads to the successive dissolution of a hierarchical system arrangement in the classical automation pyramid to an automation cloud, in which entities such as CPS are used [1] Enterprise Level (ERP) IoTS Control Level (MES) Device Level (PLC) Field Level (Sensors, Actors) Components Components Classical Automation Hierarchy IoTS/CPS-based Smart Factories xUtilization of proprietary communication xUtilization of interoperable communication standards, tree networks standards, mesh networks xLimited access to devices due to strict xDecentralized, enhanced condition layered encapsulation of entities monitoring methods xLimited condition monitoring capabilities Figure From classical maintenance to IoTS-based maintenance [1] 2212-8271 © 2016 The Authors Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 49th CIRP Conference on Manufacturing Systems doi:10.1016/j.procir.2016.11.071 411 Hans Fleischmann et al / Procedia CIRP 57 (2016) 410 – 415 Nevertheless, in practice there are still significant gaps in networking various physical resources and corresponding information systems - particularly with regard to maintenance-related functions, processes and data The aspects and capabilities of CPS have thus far only been rudimentarily considered, but there is still no integrated consideration of production resources, control systems or employees in the shop floor Maintenance in a smart factory is particularly characterized by condition monitoring systems (CMS) for critical components and sophisticated assistance systems, in order to achieve a predictable and resilient CPS As a maintenance worker, who ensures the availability of capital-intensive CPS production resources, humans are even more of a success factor This is accompanied by Socio-Cyber-Physical Systems (Socio-CPS), which emphasize the integration and interaction of and collaboration between humans as well as smart machinery and lead to greater potential in the field of maintenance [3] Within the scope of Socio-CPS, various technological solutions and system architectures are currently in discussion, but to date, there is a lack for tangible, domainspecific architectures and best practices This paper introduces a holistic, modular architecture for Socio-CPS-based CMS in smart factories Following the challenges and literature review in the field of CMS and architectures for CPS, system design as well as a concept of this architecture lay the foundation for this work’s discussion We highlight a novel architecture for CPS-based condition monitoring, fault diagnosis and predictive maintenance State of the art In smart factories, CMS represent the business logic that monitor state variables and produce diagnostics of current and future errors [4] Consequently, robust and accurate condition monitoring is indispensable for autonomous production machines to create products successfully However, most CMS entail several tasks depending on the associated production machine or process, which results in varying requirements for cyber-physical computing architectures Nevertheless, according to Schenk [5], CMS are arranged according to a general processing scheme: (1) measuring and storing machine parameters that reflect the current status of the production machinery or the corresponding production processes (state detection), (2) comparison of current data with predetermined nominal data (state comparison) and (3) specific error diagnostics (diagnosis) The state detection process is the measurement and the declaration of machine parameters that reflect the current status of a production machine and its components For this purpose, sensors are used for monitoring and quality assurance This is associated with the fact that CPS have diverse types of sensors for perceiving their environment as well as their condition by definition As regards condition monitoring applications, this results in vast amounts of data from sensors concerning loads, temperatures, machine parameters and environmental conditions yet to be considered After data collection, identified machine parameters are compared with predetermined, time-dependent nominal values These nominal values are declared by the examined process parameters, while limits are usually empirically determined by the manufacturer or user of the machine Based on the results from comparing the states, potential errors can be detected earlier and more readily Hereby, the purpose of the diagnosis is to identify the errors’ and inefficiencies’ source of origin Fig summarizes the partial steps of condition monitoring CMSs have largely been studied from a theoretical to an applicative point of view Windmann et al [6] presents a novel classification schema for systems, models and model learning algorithms for diagnosis of automation systems Wollschlaeger et al [4] describes a reference architecture for condition monitoring systems In case of machine failures, maintenance workers must initially conduct a timeconsuming, root-cause analysis To find the root of the problem, production machines predominantly deliver simple and inaccurate status codes, which are the basis for the repair process Complex errors and inefficiencies depend on several parameters and may likely only arise in specific time periods that cannot be detected by conventional CMS Moreover, relevant data is either distributed over several human machine interfaces (HMI) over the entire shop floor or is only available in highly complex expert systems It is naturally important for the operator to have the data visualized correctly and with an accurate degree of granularity, which is often not the case due to condition monitoring tasks being distributed amongst various industrial information systems Moreover, the application of condition monitoring methods on production facilities has typically been continuously started anew within industry, which has proven timeconsuming and error-prone New methodologies and standards for modeling and implementing such applications are needed in order to reduce development time [4] In addition to the high degree of distribution, current CMS are tightly linked to vendors and proprietary systems, such that there is a need for CPS-based, interoperable monitoring and process control with a manual maintenance interface [7] In comparison to existing work, we highlight a tangible, shopfloor-ready condition monitoring architecture by using web technologies for platform-independent Human Machine Interfaces (HMI) In the next chapter, we will discuss related scientific work in the field of CPS and CMS value Sensor value State detection time Exceedance Upper limit CPS State comparison Lower limit Diagnosis Diagnose faults Take countermeasures SocioCPS Fig Partial steps of condition monitoring and their relationship according to CPS [5] 412 Hans Fleischmann et al / Procedia CIRP 57 (2016) 410 – 415 2.1 CPS Architectures CPS are characterized by their ability to adapt and learn: They analyze their environment and, based on observations, learn patterns, correlations and predictive models with artificial intelligence [1] Typical industrial applications are condition monitoring, predictive maintenance, image processing and diagnostics In related scientific work, highlevel approaches have been introduced for reference architectures as well as templates for implementation strategies, which show the benefits of CPS and semantic technologies for automation and smart factories [4] For example, the term Plug-and-Produce refers to CPS’ ability to react to the presence of new components and reconfigure themselves accordingly, particularly for monitoring hardware and supervising control systems [8] Additionally, the concept of the Manufacturing Service Bus adapts the idea of ServiceOriented Architectures (SOA) in the domain of industrial production and CMS [9] Architectural aspects and standardized communication are the keys to establish the interaction of entities in a cyber-physical production scenario In the field of CPS and IoTS, various communication standards have been brought forth in public discussion 2.2 Human-CPS Interaction To date, there continues to be an utter lack of standards, best practices and design patterns as to how the maintenance worker should interact with CPS on the shop floor [5] Proponents of CPS argue that maintenance will be further complemented through interaction with said maintenance workers Unfortunately, CPS-based coordination and decision-making mechanisms generally lead to cognitive overload and particularly cause problems in the interaction between employees on the shop floor and in sophisticated CPS [10] It is furthermore difficult to integrate the maintenance worker’s experiential knowledge into a SocioCPS Other untangible qualities, such as work ethic and performance, are also necessary to react appropriately to unpredictable situations This leads to a high demand for new assistance systems Frazzon [3] introduced the concept of Socio-CPS in production and reviewed the social aspects of such systems In doing so, he intends to promote future research towards Socio-CPS applied to production networks In this architecture, context-dependent behavioral aspects and implications related to the human interface are delimited The results obtained substantiate the dependence of Socio-CPS on properly considering to the issues of human interaction together with technology Smirnov [11] introduced CyberPhysical-Social Systems (CPSS), which integrate physical, cyber, and social worlds Similar to Socio-CPS, CPSS rely on communication, computation and control infrastructures that consist of several hierarchical levels with heterogeneous resources comprised of computational resources, IT services, humans and automation components Therefore, operating and configuring CPS requires techniques for managing the variability during design and the dynamics during runtime, both of which are caused by a multitude of component types and changing application environments Furthermore, there is a demand for technical assistance systems with innovative, multi-modal, human-machine interfaces, semantic search models and evaluation techniques [1] Additionally, interaction between people and CPS is not limited to modalities such as a keyboard Research in the field of mobile devices beyond conventional approaches has also been conducted, as with augmented reality data glasses and exoskeletons [10] Access to systems is even possible via touch screens, language or gestures In addition to specific tasks sought in the field of condition monitoring, maintenance and system architectures, the question of organizing and controlling Socio-CPS is of great importance The organizational science thus significantly contributes to fundamental considerations for the design of application systems to support the work processes in SocioCPS [3] Organizational and controlling problems occur through the gap between global system contexts that mainly involve coordination mechanisms for a variety of agents, and the local system context, which mainly include the direct interactions of one or fewer agents These enable the simultaneous consideration of both contexts by establishing appropriate organizational forms Here, research remains insufficient in particular in precisely configuring the forms of coordination 2.3 Smart Condition Monitoring Technical maintenance and CMS represent two additional research fields within the CPS context Otto et al [12] focused on descriptive engineering approaches and distributed architectures for CPS They showed the ability to automatically generate monitoring software based on new requirements to support cyber-physical infrastructures for industrial applications In addition to signal-based technical diagnosis, model-based methods using sensor pattern recognition have become increasingly important [6] Architecture Description 3.1 Requirements In accordance with the requirements engineering of the research project S-CPS [7], we suggest the following requirements for our CMS architecture: The architecture must primarily offer detailed condition monitoring functionalities for supporting the maintenance process Depending on the application, the kind of CPS and the industrial domain, different condition monitoring techniques are of merit Therefore, CMS should be appropriately flexible in terms of CPS-based condition monitoring techniques For precise monitoring purposes, a temporal granularity of operational data at the sub-process level is imperative Condition monitoring must perform data processing from various machine components that can adjust to the sampling frequency of the monitored components In order to monitor industrial processes with CPS, near 413 Hans Fleischmann et al / Procedia CIRP 57 (2016) 410 – 415 real-time constraint support is mandatory For this, TCP/IPbased, real-time-enabled Ethernet protocols, such as Process Field Network (PROFINET), have prevailed [1] To ensure the efficient and low-latency execution of production processes, CMS should analyze operational and sensor data locally while maintaining low network loads Streamlined communication solutions are also of necessity, whereby single CPS with intelligent mechanisms process information in advance and send events only when necessary In addition to the time behavior within industrial control systems, the use of interoperable communication standards with semantic information models and protocols is necessary in order to import and export input data and calculated results The system should be more software-defined than constrained by hardware in addition to being able to adapt to future requirements To ensure transferability and adaptability on existing systems as well as heterogeneous industrial computing platforms and automation components, such IPC or PLC, CMS must rely on novel, mainly software-based solutions In industrial manufacturing companies, machinery from various suppliers with heterogeneous communication capabilities is used Steps towards integrating such machinery into CMS should be taken Particularly in medium-sized companies, acquired machinery stocks often represent a key corporate value To ensure the applicability of the concepts for large industrial companies as well as for medium-sized companies, the Socio-CPS should be runnable by using reliable, industry-grade components on low-cost embedded devices To fulfill social requirements, certain aspects that influence HMI must be taken into consideration Firstly, in the maintenance process, there are many stakeholders involved in the company with varying roles (e.g maintenance staff, management, external service providers) This fact requires adaptability of the HMI as well as different kinds of visualizations, depending on the requirements of the various roles When developing the Socio-CPS, the interaction between the user and CPS must be central To map the interaction between an intelligent system and the maintenance worker as well as possible, two-way communication mechanisms and protocols without polling-mechanisms must also be realized 3.2 Concept In accordance with the classical methodology of condition monitoring and the new requirements from Socio-CPS, an overall architecture emerges, as depicted in Fig By categorizing the three partial steps of CMS, separating concerns, transferability on distributed computing platforms and a high degree of modularity can be reached Between the modules, unified interfaces for data streaming guarantee standardized communication and adaptability Usually, relevant data for CMS are kept in Production Data Acquisition (PDA) (I) as well as in (CPS-based) sensors (II) and actors (III) By using decentralized data acquisition on the machine level, the requirement for precise and rapid data provision can be achieved After standardized Data PDA (I) CPS-based Sensors (II) CPS-based Actors (III) Data Aquisition (IV) Condition Monitoring Engine (V) Cloud (VI) HMI (VII) Machine Interface (VIII) Maintainer IoTS Figure Block Diagram of the Condition Monitoring System Acquisition (IV), the CMS-functionalities take place in the Condition Monitoring Engine (CME) (V) In order to meet the requirements for communication, the state detection and state comparison functionalities must be decentralized We fulfill the requirements as well as the theoretical construct of intelligent CPS machines for structured monitoring and selfdiagnostics By applying this concept, the development of generic CMS with minimized communication overhead is possible For classical condition monitoring functionalities, nearly no communication to cloud-based systems is necessary Nevertheless, in an IoTS-production grid, data exchange with cloud-based applications (VI) and centralized services is still useful For example, in times of low traffic, updates, statistical classifiers or nominal values can be transferred between cloud-based services and the CME Finally, the HMI (VII) considers the integration of humans into the Socio-CPS On the one hand, the CME must have the ability to diagnose errors and push messages to a user if selfrepair is not possible On the other hand, the maintenance worker should have the ability to trigger CME-specific abilities, such as adapting and learning nominal values for sensor and operational data based on patterns and observations In order to reflect human workers and the CPS interacting, Socio-CPS must consider the possibilities of bidirectional communication technologies and responsive graphical user interfaces (GUI) Additionally, the results of the CME should be exportable into the IoTS (VIII) 3.3 Socio-CPS CME design In order to prove the applicability of the CMS architecture, we will describe the design of the proposed Socio-CPS CME Based on the concept described above, Fig shows an advanced block diagram of the CME architecture In the subsequent sections, details of the implementation are described Conforming to the Socio-CPS architecture, the CME is divided into the modules Import, Data Manager, Analysis and Provider Each module encapsulates a specific Data Acquisition (IV) CPS-based Actors (III) Parse configuration Initialize CMA Execute CMA Assemble condition monitoring results Generation Process Condition Monitoring Adapter Nominal Data CPS Web Browser Serve dashboard content for HMI Web Server Provide the results of CME Establish servers for data export Dashboard Provider IoTS (VIII) Training Data Cloud Service (VI) Data Sets Create Data Sets Cache Data Sets Provide API for data access by CMA Condition Monitoring Adapter n HMI (VII) PDA (I) Establish connections to data sources Parse condition monitoring configuration Install subscribe-mechanisms Trigger update events for Data Manager Data Manager Import CPS-based Sensors (II) Analysis Condition Monitoring Engine (V) (2) State Comparision task and exchanges data by using a standardized publishsubscribe mechanism This approach offers maximum flexibility, because it is possible to skip existing modules or insert new modules into the layers Scalability is ensured by transferring individual modules to various industrial computing platforms (Cloud Computing, PLC, etc.) In accordance with the postulated requirements, the integration of existing machinery as well as modern CPScomponents into the CME must be possible For state detection, a methodology for integrating relevant data from heterogeneous sources is necessary, such as streaming data from sensors or actors in communication protocols, or static operational data in log files or databases In this context, different data sources have varying integration demands Whereas data from SOA and standardized protocols are comparatively easy to integrate, there are proprietary protocols that have high integration requirements In our architecture, we take into account a layer model that evaluates different machine components for data acquisition If the data is available on a protocol from a higher level, the integration of protocols in the lower layers may not necessarily be mandatory Import must be customizable by using a configuration manager without programming knowledge, such that the CME can be tailored to each relevant CPS Multiple connections and Monitored Items per data source can be set and reconfigured, while each Monitored Item is a variable stored in internal data structures that contain references to specific update events The list of objects is also held in internal data structures and exposed to subsequent layers for further processing For efficient data processing in the CME, a separate layer is needed that stores Monitored Items from various CPS consistent for use with subsequent layers The Data Manager is responsible for managing and storing individual Data Sets A Data Set is created for each Monitored Item upon initialization It holds a certain time series of Data Points that is created on update events A Data Point is a set of attributes and values, in this case generated by the PDA, and is dedicated to a single Monitored Item at a specific point in time Internally, the Data Manager uses both a cache for fast data access and a database for persistent storage A special set of methods provided by the Data Manager enables straightforward data retrieval using query methods The Monitored Items data can be fetched from the Data Manager by given timestamps, time periods or quantities of data points relative to the last available data Defined queries facilitate comprehensive preprocessing by calculating different metrics such as standard deviation and mean Analysis is the core module of the CME and provides highly flexible data processing functionalities by implementing a configuration-based data linking strategy Individual Data Sets can dynamically be connected and changed to conform to the current production environment and monitoring demands The combination of specific data sources and different analysis functionalities also occur in this module The actual Analysis, data manipulation and export are conducted in dedicated sub-modules called Condition Monitoring Adapters (CMA) A CMA performs specific analysis tasks, such as predictive maintenance, condition monitoring or error diagnostics Each CMA is computed at a (1) State Detection Hans Fleischmann et al / Procedia CIRP 57 (2016) 410 – 415 (3) Diagnosis 414 Figure Advanced Block Diagram of the Socio-CPS Architecture certain update interval, which can be time- or event-based Internally, a CMA fetches the required shared data from the Data Manager module and computes the analysis result Afterwards, the return values are merged into the analysis result and optionally passed on to a subsequent CMA This enables lean, single-task data processing and, with leveraging, the chaining of CMA As explained below, artificial intelligence techniques play a crucial role in the execution of the condition monitoring functionalities In essence, these allow CPS to acquire normal operation procedures in order to detect and diagnose errors as well as inefficiencies Through design and implementation of appropriate CMA, various decentralized monitoring mechanisms can be applied in a standardized way The generated results are stored in an extended Data Set, which, in turn, is passed on to further modules In this case, the CMA is divided into a Cloud Service and a decentralized analysis within the CPS The execution of state comparison, which requires few computational resources, is performed locally on the CPS Creating and updating nominal data with a generation process is intentionally cloud-based for the following reasons: (a) Easy uploading of diagnostic intelligence to similar machines or systems, increases effectiveness, as CPS can possibly learn from several other CPS and (b) the condition monitoring functionalities also perform without network access As outlined by the requirements, the results of the Analysis are exported to the IoTS In order to address any Socio-CPS aspects, it is necessary to present the state comparison and diagnostic results in a configurable, role-specific HMI Therefore, the Provider module was established To support the demands from the different stakeholder roles and those of Hans Fleischmann et al / Procedia CIRP 57 (2016) 410 – 415 Monitored Item Monitored Item Monitored Item Monitored Item Data Set Data Set Data Set Data Set CMA CMA CMA CMA HMI HMI HMI HMI Figure Interactivity between Data Categories in the HMI Socio-CPS, every user is able to individualize the HMI By using browser-based technologies, this requirement is able to be addressed The results are exposed in separate namespaces to minimize the network traffic Data is only sent to clients that explicitly connect to an Analysis result In addition to the Analysis data, the user-specific dashboard configuration as well as the visualization widgets and their connection to the data sources are delivered in parallel to the CME data by a web server The web server also delivers static data such as Content Style Sheets (CSS) and application logic for visualization Analogous to the other modules, the dashboard is controlled by a flexible configuration, in which the widgets’ position and options are declared and linked to the Analysis results Aside from the WS-based export for web browsers, the CMS should offer relevant data via IoTS-specific protocols in industrial networks Fig shows the relationships between Monitored Items, Data Sets, CMA and the HMI Finally, the CME initializes the previously explained modules and links their data streams The modules’ loose coupling makes it possible to extend and substitute any of them A new module specifies and implements the unified interfaces for exchanging Data Sets between the related modules By means of a registration process, the orchestration of the modules is done internally New CMA can be implemented and activated in the Analysis, analogously to the integration of new modules CONCLUSION Industry 4.0 has enabled many new services and humanmachine applications along industrial value chains Currently, there is a need for sophisticated diagnostic assistance systems on the shop floor level, due to the fact that diagnostic assistance systems are primarily expert systems Socio-CPS facilitate the interaction of maintenance workers and intelligent production machines in the field of condition monitoring, error diagnostics and predictive maintenance The proposed architecture fulfills the outlined requirements of CPS and shows new interaction mechanisms in collaboration with CPS and human operators Cognitive overload in the diagnosis of complex technical systems is mitigated and manageable for the maintenance staff by using role-optimized Socio-CPS CMS, according to the proposed architecture, thus increase maintenance and repair efficiency in smart factories The basis for the standardized application of various condition monitoring functionalities was additionally able to be established The developed architecture delivers significant contributions to the ongoing design and architectural development of IoTS-specialized CMS and Industry 4.0 applications The basis for the standardized application of various condition monitoring functionalities was established Through a modularized approach and respective CMA modules, this architecture represents a starting point for further, domain-specific developments in the environment of CPS The focus on the present work lies in the design and implementation of the architecture at a test cell for electronic assemblies due to validation purposes Acknowledgements We would like to thank the Federal Ministry of Education and Research as sponsor of the research project S-CPS References [1] ten Hompel M, Vogel-Heuser B, Bauernhansl T Industrie 4.0 in Produktion, Automatisierung und Logistik: Anwendung, Technologien, Migration Wiesbaden: Springer Vieweg; 2014 [2] Deutsche Akademie der Technikwissenschaften Cyber-Physical Systems: Driving force for innovation in mobility, health, energy and production Berlin Heidelberg: Springer Vieweg; 2011 [3] Frazzon EM, Hartmann J, Makuschewitz T, Scholz-Reiter B Towards Socio-Cyber-Physical Systems in Production Networks Procedia CIRP (Setúbal, Portugal, May 29 - 30, 2013) CIRP CMS 2013 p 49-54 [4] Wollschlaeger M, Theurich S, Winter A, Lubnau F, Paulitsch C A reference architecture for condition monitoring Proceedings World Conference on Factory Communication Systems Systems (WCFS) Palma de Mallorca: 2015 p 1-8 DOI=http://dx.doi.org/10.1109/WFCS.2015.7160555 [5] Schenk M Instandhaltung 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DOI=http://dx.doi.org/10.1016/j.protcy.2014.09.08 415 ... Internally, the Data Manager uses both a cache for fast data access and a database for persistent storage A special set of methods provided by the Data Manager enables straightforward data retrieval... interfaces for data streaming guarantee standardized communication and adaptability Usually, relevant data for CMS are kept in Production Data Acquisition (PDA) (I) as well as in (CPS-based) sensors... subscribe-mechanisms Trigger update events for Data Manager Data Manager Import CPS-based Sensors (II) Analysis Condition Monitoring Engine (V) (2) State Comparision task and exchanges data by using a standardized