SAFIRE | Cloud-based Situational Analysis for Factories providing Real-time Reconfiguration Services
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-11-2015
-31-10-2017
01-09-2017
-31-08-2020
UPTIME will reframe predictive maintenance strategy in a systematic and unified way with the aim to fully exploit the advancements in ICT and maintenance management by examining the potential of big data in an e-maintenance infrastructure taking into account the Gartner’s four levels of data analytics maturity and the
proactive computing principles.
UPTIME will enable manufacturing companies to reach Gartner's four levels of data analytics maturity for optimised decision making - each one building on the previous one: Monitor, Diagnose and Control, Manage, Optimize - aims to optimise in-service efficiency and contribute to increased accident mitigation capability by avoiding crucial breakdowns with significant consequences. UPTIME Components UPTIME_DETECT & UPTIME_PREDICT and UPTIME_ANALYZE aim to enhance the methodology framework for data processing and analytics. The key role for the UPTIME_DETECT and UPTIME_PREDICT components are data scientists who are in charge of developing, testing and deploying algorithmic calculations on data streams. In this way, the component is able to to identify the current condition of technical equipment and to give predictions. On the other hand, the UPTIME_ANALYZE is a data analytics engine driven by the need to leverage manufacturers’ legacy data and operational data related to maintenance, and to extract and correlate relevant knowledge.
The "Persistence" layer in the UPTIME conceptual architecture includes a Database Abstraction Layer (DAL) and houses of relational database engine as well as a NoSQL database, where all information needed by the "User Interaction" and "Real-Time Procesing and Batch Processing" layers (refer to UPTIME conceptual architecture) is stored and retrieved. For the raw sensor data itself, this data storage concept is enhanced by a database for time-series data to ensure efficient and reliable storage, while visualization functionalities will use an indexing database to facilitate the exposure of analytics. In these databases, all information needed by the other three layers is stored and retrieved. The UPTIME solution aims to provide data harmonization in terms of manipulating streaming data coming from the sensors. Based upon these needs a time series database is needed and in the context of UPTIME three instances of influxDB (one instance per business case) are installed. Along with the influxDB instances, a common MySQL database that will handle the operations of the UPTIME system is created.
UPTIME Data are stored in appropriate, shared databases (NoSQL, time-series-based, relational) according to a common UPTIME predictive maintenance model in order to facilitate homogeneous access.
01-11-2017
-31-10-2020
01-10-2017
-31-03-2021
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.
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.
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.
01-01-2019
-31-07-2022