Z-BRE4K | Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories
Semantic/information interoperability Comments In simple terms, this means that data from various sources can be easily harmonised.
Z-BRE4K’s mission is to build a distributed software system solution including the Industry 4.0 principals towards cyber-physical, digital, virtual and resource-efficient factories. The ultimate goal is to develop intelligent maintenance systems for increased reliability of production systems. Additionally, special attention is going to be given on processes, advancing technologies and products, integrating knowledge, training, technology and industrial development in a market-oriented environment. Z-BRE4K’s intended impact to the European manufacturing industry in the increase of the in-service efficiency by 24%
Z-BRE4K will contribute to the productivity increase of different critical manufacturing processes, such as joining (GESTAMP), cutting (PHILIPS) and forming (GESTAMP, PHILIPS, SACMI) by providing analytics and suggestion in to order to assist in minimizing the machines breakdowns. The main gain is operational and maintenance costs reduction. Furthermore, for the GESTAMP use case a real-time arc welding quality control system, based on infrared images, is being developed
Z-break will make it possible to combine the current manufacturing systems with current and new mechatronic systems. These combinations will lead to smarter manufacturing systems and thus a shorter ramp up in generating higher quality and productivity.
Part of the Z-BRE4K project is the development, of a novel embedded condition monitoring solution with cognitive capabilities, by applying deep learning techniques to reduce the dimensionality of multimodal sensor data associated to a given machine/device, and provide meaningful features to predictive maintenance services on the cloud. Most suitable IoT edge devices, for optimal trade-off between computational power and energy consumption, sensors, providing relevant information of the condition of different components, and signal processing algorithm are proposed for different machines and processes. Data gathering is enabled by the installation of IoT gateways, where data in different protocols are homogenised and sent to the cloud for storage. Real-time data, relevant KPIs and information about components status are visualised through dedicated dashboards.
The Z-Break solution uses a variety of communication protocols. HTTP, OPC-UA, IEEE 802.15.4e and IEC WirelessHART. The Hypertext Transfer Protocol (HTTP) is an application protocol for distributed, collaborative, hypermedia information systems. HTTP is the foundation of data communication for the World Wide Web. OPC UA supports two protocols. The binary protocol is opc.tcp://Server and http://Server is for Web Service. Otherwise OPC UA works completely transparent to the API. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs). It specifies the physical layer and media access control for LR-WPANs, and is maintained by the IEEE 802.15 working group, which defined the standard in 2003. WirelessHART is a wireless sensor networking technology based on the Highway Addressable Remote Transducer Protocol (HART). Developed as a multi-vendor, interoperable wireless standard, WirelessHART was defined for the requirements of process field device networks. Also, it uses the NGSI protocol. NGSI is a protocol developed to manage Context Information. It provides operations like managing the context information about context entities, for example the lifetime and quality of information and access (query, subscribe/notify) to the available context Information about context Entities.
AUTOWARE platform will push forward and stablish an open CPPS ecosystem. In the AUTOWARE Framework, a collection of enablers has been defined as components/tools that will enable potential users of the tools, be it end users, system integrators or software developers, to easily apply the developed technical enablers to their daily work. Moreover, verification, validation and certification enablers will be introduced in the AUTOWARE platform.
In the framework of Z-BRE4K, an IoT approach is applied to integrate end user machines to the Z-BRE4K platform. Through IoT gateways deployed at the shop floor, machine components are enabled to communicate their conditions, sending sensors data to the cloud where they are stored and analysed to provide predictive maintenance related information.
The UI’s goal is to visualize data from maintainers and components statuses (real time data and relevant KPIs). The SPARQL Web service is used to send custom SPARQL queries against the Semantic Framework RDF repository as a general-purpose querying web service. The UI can visualise the probability of breakdown and RUL. CAD and CAE models would be useful in mapping these values into a 3D visualisation.
Z-Bre4k provides a Semantic Framework as a RESTful web services API. Each request returns an HTTP status and optionally a document of result sets. Each results document can be serialized and may be expressed as RDF, pure XML or JSON. The operator input to the machine and threshold changes can be built as a UI. These parameters can be monitored directly in machine simulators. Furthermore, the dashboard application (M3 modules) will alert the shop floor operator about quality detected issues and suggests recommendations for the production adjustment and maintenance of the machine.
Z-BRE4K solution provides a big data analytics framework for the identification of the deterioration trends to extended towards prescriptive maintenance. Advanced data analysis tools are under development, to be applied to the quality and production data to realise zero-defect and zero-break down production. Furthermore, it involves models for anomaly detection, that are capable of identifying the machine states where the operation deviated from the norm. This is achieved by collecting the data from the machine sensors in chunks of time and processing them in batch through deep learning models. The models are trying to recreate their inputs, and this results in an observable measure called Reconstruction Error, which is used to identify states that the models aren’t capable of addressing sufficiently (which constitutes an anomaly.
The suggestion beyond the state-of-the-art is to have intelligent machine simulators so an information and knowledge rich platform can provide an accurate account of the machine’s current state and provide predictive (look ahead) potential scenarios of future time type, severity and risks of breakdown. Collected, processed, integrated and aggregated data will be structured and fed in real-time into networked simulators enabling advanced analysis and visualization to provide smart services, higher fidelity and prediction accuracy for production and manufacturing assets management. Different schemes for data collection configuration are implemented (ranging dedicated IoT devices with independent methodologies) to collect raw data from sensors, pre-process and aggregate the information, and share the results with other services through an IDS connector. The Z-Bre4k IDS connectors have a reference architecture to ensure data sovereignty and integrity throughout this collection phase.
Within Z-BRE4K, a novel software application will be developed and added to I-LiKe Machine’s tech stack: A Knowledge Base System (KBS) to extract, store, and retrieve all the relevant information enriched with semantic annotations to guarantee a prompt identification of criticalities in the process.
The KBS represents a step towards the implementation of novel and innovative solutions, still not common practice in manufacturing. The data repository is in the form of Triplestore which is designed to store identities conducted from triplex collections representing a subject-predicate-object relationship. On top of the repositories a reasoning engine creates relationships and allows to extract knowledge to be consumed by other applications.
Z-BRE4K solution will provide an ontology with annotation mechanisms that include all the necessary information to perform predictive maintenance analysis to achieve extended operating life of assets in production facilities. This context includes the sensorial data processing to be used as simulation inputs and the simulation process itself (physics-based modelling). It also includes the machine learning application as well, due to the usage of prediction models in data-driven modelling. Knowledge Based System (KBS) will extract, store and retrieve all the relevant information enriched with semantic annotations to guarantee a prompt identification of criticalities. Shoop floor data is transformed into RDF (Resource Description Framework) data, a standard model for data interchange on the web, and stored in a triple store DB. Also, the M3 Gage platform serves for fast verification of the machine, condition monitoring, and as a data repository as well. It allows information interconnection from different data sources, and furthermore, the architecture proposed by AUTOWARE provides the ability to establish data processing and computing at the most appropriate abstraction level in the hierarchy: Field, Workcell/ Production Line, Factory and Enterprise. Different filtering and pre-processing algorithms are applied on the edge to clean real time raw data and reduce unwanted noise. In addition, convolutional neural networks are used to process high-throughput video streams, providing a non-time critical stream of features for cloud services.
Within Z-BRE4K a semantic data modelling is used for interoperability. Semantic representation is used for machinery, critical components, failure modes as well as optimal conditions. Statistical methods and machine learning algorithms are used in offline mode to discover patterns in the data and associate them with specific events (Pattern discovery, Association Rules), as well as infer causality events in cases such as quality control (Quality Estimation based on Machine Status.
The modelling and simulation methods used in Z-BRE4K are mainly Finite Element Methods (FEM) where complex problems and processes from the real world are being simplified and solved using a numerical approach. First, an accurate digital model of the geometry and material properties of all involved objects, boundary conditions between these objects and process data is created (i.e. forces or temperature).
Then, the complex shape of all objects involved, is approximate using a finite number of simple geometries (i.e. triangles) which simplify the complex mathematical problem. A computer is capable of solving these mathematical operations at a rate impossible for humans and thus enables the user to analyse various scenarios, ranging from mechanical strains within the objects to rise in temperature or material fatigue. This information can be used to predict the remaining useful lifetime of a given tool.
Simulation platform is deployed by the physical equipment to create intuitive maintenance control and management systems. The Z-BRE4K’s platform simulation capabilities will estimate the remaining useful life calling for maintenance and suggesting the optimal times to place orders for spare parts, reducing the related costs. The increased predictability of the system and the failure prevention actions will reduce the number of failures, maximise the performance, reduce the repair/recover times reducing further the costs.
By applying time series analysis, we are able to detect special events that are known (Fault detection) or unknown (anomaly detection) during production. This information, correlated with sensor readings is fed into machine learning algorithms that create estimates of Remaining Useful Life (RUL), Health Indexes (HI) and forecast upcoming events (Likelihood of Failure). Special focus is given in techniques that can provide real-time information (Fast computation and high accuracy) as well as being scalable in order to use new data as it becomes available. Additional information such as meantime between failures based on historical data or an expert opinion, CAE data, quality control data, real time states etc. are also used to the design of machine simulators.
The AUTOWARE apps development will be supported and linked to the FIWARE. AUTOWARE approach is to connect and extend the FIWARE for Industry resources and assets to the end of digital automation community, so synergies emerge both in terms of multi-sided business opportunities and amount of resources that are made available to the cognitive automation community to build their autonomous solutions and apps.
FIWARE is a curated open source framework with components that can be assembled together with other third-party ones to accelerate the development of Smart Solutions. The Orion Context Broker is the core component of any “Powered by FIWARE” platform. It enables the system to perform updates and access to the current state of context.
The choice of the AUTOWARE platform was based on the fact that is an open source project, and that is hardware agnostic.
The Z-Bre4k solution is based on the blackboard architectural model. This model is mainly an artificial intelligence approach, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts. The blackboard component acts as a central repository system. The rest of the software applications (components) act independently at the common data structure stored on the blackboard, they respond on changes and create new reactions according to changes. Interaction between components is implemented via the blackboard.
Orion is a C++ implementation of the NGSIv2 REST API binding developed as a part of the FIWARE platform that allows the management of the entire lifecycle of context information including updates, queries, registrations and subscriptions. It is an NGSIv2 server implementation to manage context information and its availability allowing subscription to context information so when some condition occurs notifications are sent. The Industrial Data Space foster secure data exchange among its participants, while at the same time ensuring data sovereignty for the participating data owners. The architecture of the Industrial Data Space does not require central data storage capabilities but follows a decentralized approach, meaning that data physically remain with the respective data owner until they are transmitted to a trusted party. Thus, the Industrial Data Space is not a cloud platform, but an architectural approach to connect various, different platforms.
The proposed software-defined autonomous service platform is an important framework for SMEs and other manufacturing organizations to transition to technologically advanced Industry 4.0 solutions. The main target of AUTOWARE are industrial manufacturing systems.
AUTOWARE reduces significantly the access complexity to the different isolated tools and speeds up the process by which multi-sided partners can meet and work together.