TWIN-CONTROL | Twin-model based virtual manufacturing for machine tool-process simulation and control
SYMBIO-TIC | Symbiotic Human-Robot Collaborative Assembly: Technologies, Innovations and Competitiveness
Fortissimo 2 | Factories of the Future Resources, Technology, Infrastructure and Services for Simulation and Modelling 2
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.
In UPTIME, two data processing solutions are considered. (1) Batch processing of data at rest, through massively paralle processing, (2) real-time processing of data in motion, real time data from heterogeneous sources are processsed as a continuous "stream" of events (produced by some outside system or systems), and that data processing occurs so fast that all decisions are made without stopping the data stream and storing the information first.
UPTIME main functionalities are structured in three main modules, namely: edge, cloud and GUI modules.
- The UPTIME edge module will ensure data collection from machines, sensors, etc. and sent it on for analysis. It may also include some additional functionalities which require real-time results.
- The UPTIME Cloud module contains all the advanced functionalities of the solution, which do not require a real time result. There we will analyse the data collected on the edge, as well as data received from relevant information systems, and provide the expected predictions. “Cloud” can refer to remote servers or an internal cloud within the customer’s Plant or Enterprise, as is deemed necessary by the customer.
- Lastly the GUI module, through which the user will interact with the previously mentioned functionalities, whether it is to view data or configure the solution.
4 main components in the cloudbased infastructure of the UPTIME platform include:
- UPTIME_DETECT and PREDICT component (extended version of PreIno prototype) processes mainly timeseries?based data from the field, to give further context to the data, e.g. to detect topical conditions of technical equipment and to predict probable future conditions.
- UPTIME_ANALYZE (a new developed prototype) 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.
- UPTIME_DECIDE component (extended version of PANDDA prototype) that implements a prescriptive analytics approach for proactive decision making in a streaming processing computational environment. It provides real-time prescriptions fo the optimal maintenance actions and the optimal time for their implementation on the basis of streams of predictions about future failures.
- UPTIME_FMECA (extended version of DRIFT prototype) provides estimation of possible failure modes and risk criticalities evolution through its data-driven FMECA approach.
UPTIME_SENSE component (USG prototype) is located in the edgebased infrastructure of the UPTIME platform. It aims to capture data from a high variety of sources and cloud environments. SENSE brings configurable diagnosis capabilities on the edge, e.g. for real-time or off-the-grid applications. SENSE addresses 3 high level functions:
- Sensor signal processing, which collects the signals from equipment or other sensors, and pre-processes them before transmitting them on the cloud platform.
- Edge diagnosis for optional state detection diagnosis for certain use cases.
- Support functions for functions necessary for the correct operation of the edge module.
COALA | COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence