UPTIME | UNIFIED PREDICTIVE MAINTENANCE SYSTEM

Summary

UPTIME will seek to reframe predictive maintenance strategy by proposing a unified framework and to create an associated unified information system in alignment to the aforementioned framework. Therefore, UPTIME will extend and unify new digital, e-maintenance services and tools in order to exploit the full potential of a predictive maintenance strategy with the UPTIME solution, will deploy and validate the UPTIME solution in the manufacturing companies participating in the UPTIME consortium and will diffuse the UPTIME solution in the manufacturing community.

UPTIME will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data with respect to the implementation of a predictive maintenance strategy. Moreover, production, quality and logistics operations driven by predictive maintenance will benefit from UPTIME.

UPTIME will enable manufacturing companies to reach Gartner's level 4 of data analytics maturity (optimized decision-making) in order to improve physically-based models and to synchronise maintenance with quality management, production planning and logistics options. In this way, it will optimize in-service efficiency through reduced failure rates and downtime due to repair, unplanned plant/production system outages and extension of component life.

Moreover, it will contribute to increased accident mitigation capability since it will be able to avoid crucial breakdown with significant consequences. Consequently, UPTIME will exploit the full potential of predictive maintenance management and its interactions with other industrial operations by investigating a unified methodology and by implementing a unified information system addressing the predictive maintenance strategy.

Results, demos, etc. Show all and search (43)
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Demonstrators, pilots, prototypes
Comment:

The UPTIME Platform is deployed and validated against three industrial use cases:  (1) production and logistics systems in the aviation sector - FFT, (2) white goods production line - WHIRLPOOL  and (3) cold rolling for steel straps - MAILLIS.

The FFT use case deals with maintenance in highly complex transportation asset operations, some of which are mobile and subjected to diverse environments and a wide range of unpredictable environmental stress factors. Due to the critical nature of the deployment of these assets, the requirements are both technically and organisationally very high. The need has arisen to increase efficiency in maintenance execution as well as in reporting to the client, who in most of FFT projects is responsible for the logistics coordination of their assets.

The Whirlpool use case deals with the newly installed production line for clothes dryers, a complex automatic production line, which puts steel coils through a sequence of several steps of straightening, punching, seaming, flanging and screwing to eventually form the drum that holds and rotates the clothes in the dryer. The drum production equipment is very complex, highly automated and critical from many perspectives. It is critical to ensure the highest possible quality of the drum, which is the core component of the clothes dryer. At the same time, it is vital to keep the production equipment running efficiently and keep costs under control.

The Maillis use case deals with cold rolling mill for the production of steel strapping. MAILLIS‘s offer combines high-quality packaging materials and state?of?the?art technology, ensuring that metal producers enjoy reliable, durable, proven and high?speed strapping and wrapping technology at the optimal cost. MAILLIS uses cold rolling mills to produce rolling products with the smallest possible thickness tolerances and an excellent surface finish. The demand for changing over the milling rollers comes from either their regular wear or from an unexpected damage, which can occur due to either a defective raw material or an equipment malfunction. This usually happens every eight hours for the work rolls and every week for the backup rolls. After several regrinding, the diameter of the roll becomes so small that the rolls are no longer useful. It is expected to have a machine that reports its current health status along with the appropriate data analytics and metrics. UPTIME should also allow predictions about equipment‘s future health as well as recommendations for future actions and enable machines performing self-assessment, on which decision-making can be followed to advance equipment maintenance and facilitate the machine components and products life cycle.

Attached files (1)
File Type
UPTIME Project Overview.pdf PDF
More information & hyperlinks
Web resources: https://www.uptime-h2020.eu/
https://cordis.europa.eu/project/id/768634
https://www.linkedin.com/in/uptime-h2020/
https://twitter.com/uptimeH2020
https://www.youtube.com/channel/UCqHA62sd4zxc8-knPDREcAw
Start date: 01-09-2017
End date: 28-02-2021
Total budget - Public funding: 6 248 367,00 Euro - 4 847 836,00 Euro
Twitter: @uptimeH2020
Cordis data

Original description

UPTIME will seek to reframe predictive maintenance strategy by proposing a unified framework and to create an associated unified information system in alignment to the aforementioned framework. Therefore, UPTIME will extend and unify new digital, e-maintenance services and tools in order to exploit the full potential of a predictive maintenance strategy with the UPTIME solution, will deploy and validate the UPTIME solution in the manufacturing companies participating in the UPTIME consortium and will diffuse the UPTIME solution in the manufacturing community.
UPTIME will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data with respect to the implementation of a predictive maintenance strategy. Moreover, production, quality and logistics operations driven by predictive maintenance will benefit from UPTIME. UPTIME will enable manufacturing companies to reach Gartner’s level 4 of data analytics maturity (“optimized decision-making”) in order to improve physically-based models and to synchronise maintenance with quality management, production planning and logistics options. In this way, it will optimize in-service efficiency through reduced failure rates and downtime due to repair, unplanned plant/production system outages and extension of component life. Moreover, it will contribute to increased accident mitigation capability since it will be able to avoid crucial breakdown with significant consequences.
Consequently, UPTIME will exploit the full potential of predictive maintenance management and its interactions with other industrial operations by investigating a unified methodology and by implementing a unified information system addressing the predictive maintenance strategy.

Status

CLOSED

Call topic

FOF-09-2017

Update Date

27-10-2022
Geographical location(s)
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Comment:

UPTIME will develop a versatile and interoperable unified predictive maintenance platform for industrial & manufacturing assets from sensor data collection to optimal maintenance action implementation. Through advanced prognostic algorithms, it predicts upcoming failures or losses in productivity. Then, decision algorithms recommend the best action to be performed at the best time to optimize total maintenance and production costs and improve OEE.

UPTIME innovation is built upon the predictive maintenance concept and the technological pillars (i.e. Industry 4.0, IoT and Big Data, Proactive Computing) in order to result in a unified information system for predictive maintenance. UPTIME open, modular and end-to-end architecture aims to enable the predictive maintenance implementation in manufacturing firms with the aim to maximize the expected utility and to exploit the full potential of predictive maintenance management, sensor-generated big data processing, e-maintenance, proactive computing and industrial data analytics. UPTIME solution can be applied in the context of the production process of any manufacturing company regardless of their processes, products and physical models used.

Key components of UPTIME Platform include:

  1. SENSE deals with data agreegation from heterogeneous sources and provides configurable diagnosis capabilities on the edge.
  2. DETECT deals with intelligent diagnosis to provide a reliable interpretation of the asset's health.
  3. PREDICT deals with advanced prognostic capabilities using genering or tailored algorithms.
  4. ANALYZE deals with analysis of maintenance-related data from legacy and operational system.
  5. FMECA (Failure Mode, Effects and Criticality Analysis) deals with estimation of possible failure modes and risk criticalities evolution.
  6. DECIDE deals with continuously improved recommendations based on historical data and real-time prognostic results.
  7. VISUALIZE deals with configurable visualization to facilitate data analysis and decision making.
Comment:

One of the main learnings is that Data quality needs to be ensured from the beginning of the process. This implies spending some more time, effort and money to carefully select the sensor type, data format, tags, and correlating information. This turns to be particular true when dealing with human-generated data. It means that if the activity of input of data from operators is felt as not useful, time consuming, boring and out of scope, this will inevitably bring bad data.

Quantity of data is another important aspect as well. A stable and controlled process has less variation. Thus, machine learning requires large sets of data to yield accurate results. Also this aspect of data collection needs to be designed for example some months, even years in advance, before the real need emerges.

This experience turns out into some simple, even counterintuitive guidelines:

1. Anticipate the installation of sensors and data gathering. The best way is doing it during the first installation of the equipment or at its first revamp activity. Don’t underestimate the amount of data you will need, in order to improve a good machine learning. This of course needs also to provide economic justification, since the investment in new sensors and data storing will find payback after some years.

2. Gather more data than needed.
A common practice advice is to design a data gathering campaign starting from the current need. This could lead though to missing the right data history when a future need emerges. In an ideal state of infinite capacity, the data gathering activities should be able to capture all the ontological description of the system under design. Of course, this could not be feasible in all real-life situations, but a good strategy could be populating the machine with as much sensors as possible.

3. Start initiatives to preserve and improve the current datasets, even if not immediately needed. For example, start migrating Excel files distributed across different PCs into common shared databases, taking care of making a good data cleaning and normalization (for example, converting local languages descriptions in data and metadata to English).

Finally, the third important learning is that Data Scientists and Process Experts still don’t talk the same language and it takes significant time and effort from mediators to help them communicate properly. This is also an aspect that needs to be taken into account and carefully planned. Companies need definitely to close the “skills” gaps and there are different strategies applicable:

  1. train Process Experts on data science;

  2. train Data Scientists on subject matter;

  3. develop a new role of Mediators, which stays in between and shares a minimum common ground to enable clear communication in extreme cases.

Result items:

Quantity and quality of data: the available data in the FFT use case mainly consists of legacy data from specific measurement campaigns. The campaigns were mainly targeted to obtain insights about the effect of operational loads on the health of the asset, which is therefore quite suitable to establish the range and type of physical parameters to be monitored by the UPTIME system. UPTIME_SENSE is capable of acquiring data of mobile assets in transit using different modes of transport. While this would have been achievable from a technical point of view, the possibility to perform field trials was limited by the operational requirements of the end-user. Therefore, only one field trial in one transport mode (road transport) was performed, which yielded insufficient data to develop useful state detection capability. Due to the limited availability of the jig, a laboratory demonstrator was designed to enable partially representative testing of UPTIME_SENSE under lab conditions, to allow improvement of data quantity and diversity and to establish a causal relationship between acquired data and observed failures to make maintenance recommendations. 

 

Installation of sensor infrastructure: during the initial design to incorporate the new sensors into the existing infrastructure, it is necessary to take into consideration the extreme physical conditions present inside the milling station, which require special actions to avoid sensors being damaged or falling off. A flexible approach is adopted, which involves the combination of internal and external sensors to allow the sensor network prone to less failure. Quantity and quality of data: it is necessary to have a big amount of collected data for the training of algorithms. Moreover, the integration of real-time analytics and batch data analytics is expected to provide a better insight into the ways the milling and support rollers work and behave under various circumstances. 

 

Quantity and quality of data need to be ensured from the beginning of the process. It is important to gather more data than needed and to have a high-quality dataset. Machine learning requires large sets of data to yield accurate results. Data collection needs however to be designed before the real need emerges. Moreover, it is important having a common ground to share information and knowledge between data scientists and process experts since in many cases they still don’t talk the same language and it takes significant time and effort from mediators to help them communicate properly. 

 

Result items:
Comment:

The standardisation goal in UPTIME is to simplify the integration of the components in the the UPTIME Platform and to make easier the integration of the UPTIME Platform in new industrial environments.

Below list of some relevant standards to UPTIME:

  • IEC 62541 (OPC-UA) and ISO/IEC 20922 (MQTT) for  Modular Edge Data Collection & Diagnosis   - also JSON, XML, MSGPACK, I2C, SPI
  • IEEE 802.15.4 for low rate personal area networks
  • ISO 13374, MIMOSA OSA-CBM and OSA - EAI for Mapping, Extraction and Analysis of Legacy DB, Configurable Diagnosis, Configurable Prognosis, Prescriptive Analytics for Proactive Decision Making
  • IEC 60812 FMEA and FMECA for Maintenance Actions Parametrization and Management interface
  • EN 13306 for common maintenance terminology
  • ISO 17359 for condition monitoring and diagnostics on machine
  • ISO 13373 for vibration monitoring, ISO 18435, ISO 10303, ISO 15926 for interoperability
  • ISO 14224 for maintenance data in oil and gas
Result items:

The Foresee Cluster Roadmap document includes section 5 on ‘Standardization aspects of Predictive Maintenance’ and ANNEX I  ‘Standards application in ForeSee projects’:

  1. Standardisation Overview 
  2. Views on Maintenance standards and Predictive Maintenance 
  3. Maintenance terminology 
  4. Evaluation of Standards 
  5. Future Activities 
Comment:

Open Platform Communication Unified Architecture (OPC-UA) is considered for UPTIME platform and for modular edge data collection and diagnosis of UPTIME_SENSE component.

Comment:
  • ISO TC 184 SC4 and  SC5 on industrial data, ISO/IEC JTC1 on Information Technology, ISO TC 184 IEC TC 65 JWG 21 on Smart Manufacturing  Reference Model - in link with RAMI 4.0
  • IOF, Industry Ontologies Foundry, ontologies for Industry particularly the working group on maintenance ontologies
  • In contact with MIMOSA e.g. for ISO 18101 for Oil and Gas Interoperability
Comment:

UPTIME is planning with other 5 FoF projects in ForeSee Cluster on common activities towards SDOs to be more efficient in the standardisation activities.

Comment:

UPTIME will provide a unified predictive maintenance management framework and a smart predictive maintenance information system covering the whole prognostic lifecycle. It will contribute to improve smart predictive maintenance systems capable to integrate information from many different sources and of various types, in order to more accurately estimate the process performances and the remaining useful life.

Comment:

In UPTIME Whirlpool Business Case, each sensor is directly connected to the respective PLC (Programmable Logic Controller), which is on board of the specific equipment. The internal SCADA system is then gathering the data from each PLC and send them to Whirlpool MOM software, which in turn stores them into the database (SQL Server).

Comment:

The economic impact of UPTIME is the most important one and can be seen at 2 levels :

  • Short/mid-term impact: improvement of financial results of industrial companies and their competitiveness thanks to better operational performance (optimized maintenance and production)
  • Mid/long term impact: the improved competitiveness will help to reconquer some market shares and then reinforce an offensive marketing strategy (low margin segments could be reprioritized)
Comment:

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.

Comment:

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.

Comment:

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.

Comment:

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.
Comment:

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.
Comment:

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.
Comment:

The UPTIME_SENSE component is responsible for the acquisition of sensor data from the field. It is utilised to enable previously disconnected assets, to communication with the UPTIME Cloud.

Comment:

The current draft of the UPTIME data model is designed based on international standards like MIMOSA (OSA-CBM v3.3.1 and OSA-EAI v3.2.3a), the initial historical data provided by the business cases and the previous implementations of UPTIME_FMECA and UPTIME_DECIDE.

Comment:

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.

Comment:

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.

Comment:

UPTIME has a common MySQL database that will handle the operations of the UPTIME system.

Comment:

UPTIME_ANALYZE is a data analytics engine driven by the need to leverage manufacturers’ legacy data and operational data related to maintenance, as well as to extract and correlate relevant knowledge. The data mining and analytics of ANALYZE component practically delivers the intelligence of the ANALYZE component by defining, training, executing and experimenting with different machine learning algorithms.

Comment:

UPTIME_VISUALIZE (extended version of SeaBAR prototype) component is responsible for the intuitive and uninterrupted human-machine interaction. The user interfaces, including the analytics dashboards and the notificaiton engine, will be customised or further developed in full accordance with the end-user business case. Taking an example ofthe  UPTIME White Good business case for complex automatic production line to produce drums for dryer, the generation of early warnings to suggest autonomous activities to factory workers should be communicated through mobile devices or on-board devices.

Comment:

The data visualisation in UPTIME is performed by the UPTIME_VISUALIZE (SeaBAR prototype) component:

  • By establishing standardised connectors to the key data/information/knowledge sources for maintenance, production planning, logistics and quality management.
  • By faceting, filtering and semantic structuring of the collected data according to maintenance viewpoints.
  • Context-sensitive, interactive visualizations in order to allow the end user to easily search and navigate through huge amounts of heterogeneous information with the aim to enable a maximum flexible analysis of all relevant information, e.g. to drill-down into the data according to a region, timeframe and machine or to generalise a specific critical situation and find similar (past) situations with appropriate measures and individual user experience (e.g. best practices).
  • A customizable dashboard focusing on the specific information needs of e.g. maintenance engineers, quality managers and upper management by different context specific visualization and analytics tools.
  • Continuous visualization of critical data and utilization of captured experience from past situations for providing useful insights in interaction with manufacturing companies’ systems.
  • By turning from generic statistical data towards instance-specific data and enabling instance specific diagnosis and prognosis.
Comment:

UPTIME Platform is developed accroding to unified predictive maintenance framework and an associated unified information system to enable the predictive maintenance strategy implementation in manufacturing industries. The UPTIME predictive maintenance system will extend and unify the new digital, e-maintenance services and tools and will incorporate information from heterogeneous data sources, e.g. sensors, to more accurately estimate the process performances. The UPTIME predictive maintenance platform is developed mainly based on five baseline e-maintenance services and tools:

  • USG (Universal Sensor Gateway): USG serves as a modular data acquisition and integration device to the Product Lifecycle Management (PLM) ecosystem of a product
  • preInO: The preInO Processing Engine is able to detect and predict the state of a whole system or components with respect to mechanical systems such as windturbines, special-purpose vehicles, production machinery, etc.
  • PANDDA (ProActive seNsing enterprise Decision configurator DAshboard): PANDDA is a software service that implements (i) proactive decision methods to provide recommendations about mitigating (perfect or imperfect) maintenance actions and the time for their implementation on the basis of real-time prognostic information; and (ii) a Sensor-Enabled Feedback (SEF) mechanism for continuous improvement of the generated recommendations.
  • SeaBAR (Search Based Application Repository): SeaBAR is a modular software platform built on Big Data and Enterprise Search technology. The SeaBAR platform supports end users by means of data aggregation, data analysis and visualization.
  • DRIFT (Data-Driven Failure Mode, Effects, and Criticality Analysis (FMECA) Tool): DRIFT is a tool that, on the basis of the information gathered in other modules (maintenance data, production, logistics, quality data) use them to identify what are the Failure Modes, Effects and Criticalities of the components and system according to literature available and novel correlation algorithms among modes failures, effects and critical impacts.
Comment:

To ease integration of all UPTIME components, the main programming language used by the components and the integrated platform is Java.

Comment:

The UPTIME platform will leverage crucial, and often hidden, data from machines and systems in real time and substantiate the benefits of advanced predictive maintenance analytics in boosting asset availability and service levels in manufacturing operations. Moreover, UPTIME delivers new ways for effective operational risk management in terms of preventing unexpected failures of a manufacturer’s assets, and effectively planning maintenance actions, thereby transforming the mentality of manufacturing industries in respect to maintenance services. With the help of the end-to-end UPTIME smart diagnosis-prognosis-decision making methods, manufacturers become leaner, more versatile and better prepared to act upon accidents and unexpected incidents in their everyday operations. The increased accident mitigation capabilities they acquire, allow them not only to accelerate the workplace safety and improve the workers’ health,
but also to reduce the incurring costs and become more competitive.

Comment:

UPTIME platform focusses on the use of condition monitoring techniques, e.g. event monitoring and data processing systems, that will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data and to handle the real-time data in complex, dynamics environement in order to get meaningful insights and to decide and act ahead of time to resolve problems before they appear, e.g. to avoid or mitigate the impact of a future failure, in a proactive manner. Moreover, UPTIME proposed unified framework will not be limited to monitoring and diagnosis but it aims to cover the whole prognostic lifecycle from signal processing and diagnostics till prognostics and maintenance decision making along with their interactions with quality management, production planning and logistics decisions.

Comment:

Open Platform Communication Unified Architecture (OPC-UA) is considered for UPTIME platform and for modular edge data collection and diagnosis of UPTIME_SENSE component.

Comment:

The UPTIME platform is built upon the predictive maintenance concept, the technological pillars (i.e. Industry 4.0, IoT and Big Data, Proactive Computing) and the existing baseline tools (i.e. USG, preInO, PANDDA, SeaBAR, DRIFT) resulting in a unified information system for predictive maintenance. The extended UPTIME baseline tools (SENSE, DETECT, PREDICT, DECIDE, ANALYZE, FMECA, VISUALIZE) will address the various steps of the unified predictive maintenance approach and will incorporate interconnections with other industrial operations related to production planning, quality management and logistics management.

Comment:

Open Platform Communication Unified Architecture (OPC-UA) is considered for UPTIME platform and for modular edge data collection and diagnosis of UPTIME_SENSE component.

Comment:

To ensure secure access, the UPTIME Platform offers appropriate authorization and authentication mechanisms. These are based on the JWT technology and are implemented by using the Spring Security framework. Currently, JWT is used to ensure a secure log-in; as components are iteratively integrated. JWT will also be used to ensure secure communications between components.

Comment:

The UPTIME conceptual architecture was designed according to the ISO/IEC/IEEE 42010 “System and software engineering – Architecture description” and mapped to RAMI 4.0 in order to ensure that it can be represent predictive maintenance in the frame of Industry 4.0.

The UPTIME vision converges and synthesizes predictive maintenance, proactive computing, the Gartner’s levels of industrial analytics maturity and the ISO 13374 as implemented in MIMOSA OSA-CBM in order to create a consistent basis for a generic predictive maintenance architecture in an IoT-based industrial environment. In this way, the Operational Technology and the Information Technology can also be converged in the context of Industry 4.0.

Comment:

One of the main functionalities of UPTIME Platform is the batch data analytics implemented by UPTIME_ANALYZE component to analyse maintenance-related data from legacy and operational data and UPTIME_FMECA component that provides estimation of possible failure modes. The interoperability interfaces with UPTIME End-Users' (e.g. Whirlpool) legacy systems are defined, specified and developed according to latest practices and standards for APIs.

Comment:

UPTIME will provide a unified predictive maintenance management framework and a smart predictive maintenance information system covering the whole prognostic lifecycle. The UPTIME solution will be applicable to any production system incorporating sensors and will be based on real-time reliability-related (prognostic) information in order to reduce the equipment downtime and malfunctions with the aim to produce high-quality products with optimized losses. It will utilize sensors for measuring various parameters of the production process, provide diagnostic outcomes, i.e. the current equipment health state, generate predictions about future equipment behaviour, and recommend optimal actions at optimal times. It will also incorporate a continuous improvement mechanism for continuous learning of Diagnosis, Prognosis and Maintenance Decision Making phases triggered by sensor data during maintenance and other operational actions implementation. The elimination of unexpected failures will lead to an increased level of safety in the workplace and to improved overall operations efficiency.

Comment:

UPTIME will be able to be applied in the context of the production process of any manufacturing company regardless their processes, products and physical model used. It will take advantage of predictive maintenance management, industrial IoT and big data, as well as proactive computing and the e-maintenance concept in order to reframe predictive maintenance strategy and to create a unified information system in alignment to the new predictive maintenance strategy framework and to Gartner’s 4 levels of data analytics maturity.

UPTIME will be applicable at the level of component, machine and production system, depending on the placement of sensors throughout the production lifecycle and the data availability in the manufacturing company’s systems (e.g. Enterprise Resources Planning- ERP, Manufacturing Execution System- MES). Within UPTIME, there will be interactions between the various e-maintenance services and the e-operations data and information from the manufacturing companies’ systems in order to synchronise maintenance with production, quality and logistics management. The results of the UPTIME solution will be evaluated by the manufacturing companies participating in the consortium and will be demonstrated in manufacturing companies beyond the consortium.

Comment:

UPTIME aims to deliver novel e-maintenance services and tools to support the daily work of maintenance engineers as well as the overall maintenance management with the aim to optimize in-service efficiency. UPTIME solution consists of extended e-maintenance services and tool, which will incorporate novel methods and algorithms for addressing the phases of the UPTIME framework and conclude in a novel predictive maintenance solution covering the whole prognostic lifecycle.

Comment:

The UPTIME solution will combine and extend existing predictive maintenance tools and services (USG/BIBA, preIno/BIBA, PANDDA /ICCS, SeaBAR/Pumacy, and DRIFT/RINA-C) and will define the way for its implementation 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.

Extended version of USG will implement the Signal Processing phase; extended version of preInO will address the Diagnosis and the Prognosis phases; extended version of PANDDA will deal with Maintenance Decision Making; extended version of SeaBAR will address the Maintenance and Operational Actions Implementation; and extended version of DRIFT will deal with data-driven FMECA.

Each extended UPTIME service will incorporate real-time data-driven information processing technologies and algorithms so that the integrated system is able to cover complete scenarios and fulfil the needs of the manufacturing companies participating in the consortium. The aforementioned tools will interact when necessary to the manufacturing company’s system (e.g. ERP, MES) in order to exchange data and information for scheduling production, quality and logistics activities together with maintenance activities (e.g. by using the production plan of the ERP system). Through the Continuous Improvement mechanism, UPTIME will be able to continuous learn with the aim to update and improve Diagnosis (Detect), Prognosis (Predict) and Maintenance Decision Making (Decide) phases by gathering actions-related and/ or failure-related sensor data (Act).  

Comment:

Through its unique bundle of a predictive maintenance management framework and an integrated platform, UPTIME will prevent critical asset failure, deploy resources more cost-effectively, maximize equipment uptime and enhance operations, while accelerating the quality and supply chain processes. By better diagnosing, anticipating and acting upon the asset performance and product quality, the UPTIME novel predictive maintenance framework is expected to increase in-service efficiency by at least 10% in the 3 pilot cases through combined KPIs measurements.

By investigating and demonstrating the applicability of the UPTIME predictive maintenance system in three pilot cases in different manufacturing sectors, UPTIME will contribute to a more widespread adoption of predictive maintenance and demonstrate more accurate, secure and trustworthy techniques at component, machine and system level.

Comment:

The business model canvas is a strategic management and lean startup template for developing new business models. It can be used both as an innovation and an implementation approach. The UPTIME business model canvas is prepared as a seed for the project’s exploitation activities. Exploitation of the project’s results is the primary objective of all UPTIME partners. Moving forward, the consortium commits to prepare a detailed and robust business plan and go-to-market strategy, to maximize the opportunities to exploit the methods and tools developed in UPTIME. The UPTIME Business Model Canvas has been evolved during the project in order to adapt to the project’s outcomes, the findings of a detailed market research and the decisions of the consortium.

Comment:

The UPTIME solution, consisting of extended e-maintenance services and tools, will be deployed and validated during the first wave of innovation within the manufacturing companies participating in the consortium, and its results will be diffused during the second wave of innovation including members beyond the UPTIME consortium; i.e. it will build up and expand vibrant ecosystems of providers and users of new e-maintenance, digital technologies and will foster exchanges between these providers and users.

To grow and sustain the ecosystem, UPTIME will encourage collaborative activities including learning and knowledge sharing activities, and networking events. It will design activities with recognition and awards attached to encourage desired behaviour and participation with the new e-maintenance services.

To sustain the ecosystem, UPTIME will cultivate and assess the knowledge and new forms of e-maintenance services and tools created by the ecosystem to inform new strategies, activities, roles, technologies, and business models for the future. It will provide opportunities for participants to play new roles and experiment with new activities. It will ensure that the UPTIME procedures, practices, and the technology support are available to sustain the ecosystem over time, and establish new roles related to harvesting and creating best practices in the ecosystem.

UPTIME will seek to strengthen the competitiveness of European industry by effectively building up and expanding a vibrant EU technological ecosystem for the manufacturing companies’ maintenance needs. The structure and membership of the UPTIME ecosystem will ensure that manufacturing companies of the consortium take on a driving role in the action, i.e. leading the innovation activities and liaising with end users, ensuring that the work responds to a clear market demand. Further information: https://www.uptime-h2020.eu/index.php/partner-programme/

Comment:

The Value Proposition and the Market Positioning of UPTIME are the most important pillar for the success of
the exploitation activities. The objective is to have a clear understanding of the Value Chain, the structure of the competition, the segmentation of the market, its evolutions and its drivers, the main business expectations and constraints, and finally the customer problems and pains. From this accumulated knowledge, the relevance of the UPTIME solution will be evaluated and validated through direct contact with end users beyond the industrial partners of the Consortium (interviews, surveys and/or focus groups).

UPTIME seeks to strengthen the competitiveness of European industry by effectively building up and expanding a vibrant EU technological ecosystem for the manufacturing companies’ maintenance needs.

Within UPTIME, the Predictive Maintenance Management Model and Integrated System are going to be validate in the three business cases and industries, ensuring the cross-sectorial evaluation of the performance, usability and applicability of the UPTIME methods and tools in scenarios engaging various machine types.

Comment:

UPTIME aims to exploit the full potential of predictive maintenance management and its interactions with other industrial operations by investigating a unified methodology and by implementing a unified information system addressing the predictive maintenance strategy.

UPTIME will implement scalable information processing technologies and user interaction with the system, e.g. by providing visualization of diagnostics, prognostics, recommendations, etc. UPTIME will be applicable at the level of component, machine and production system, depending on the placement of sensors throughout the production lifecycle and the data availability in the manufacturing company’s systems (e.g. Enterprise Resources Planning-ERP, Manufacturing Execution System-MES). Within UPTIME, there will be interactions between the various emaintenance services and the e-operations data and information from the manufacturing companies’ systems in order to synchronise maintenance with production, quality and logistics management.

Comment:

The primary market segment UPTIME targets is the Manufacturing Industry. First of all, the opportunity is attractive: it is a quite large market where the economic impact of predictive maintenance is high and little has been deployed so far. Second, the competitive advantage of UPTIME is high: its Value Proposition fits very well with the needs of the customers, the expertise of the members of the consortium is strong in this domain and the 3 Business Cases are relevant.

Moreover, three secondary market segments because the technologies involved in UPTIME can be deployed in their contexts:

  • Process industries: any downtime results in immediate and irreversible loss of production without any possibility to compensate
  • Oil & gas / Utilities industries: the same rationale as with the process industry
  • Aerospace & Defense MRO: major players are currently seeking to implement predictive maintenance to optimize the Total Cost of Ownership of the systems

 

UPTIME’s strategy is to focus its efforts on the core market, the manufacturing industry, but with a pragmatic approach regarding the 3 secondary segments: follow-up market demand, anticipate specific expectations and seize opportunities.

Comment:

From the end-user perspective, the main Value Proposition of UPTIME is to propose a complete solution to deploy full predictive maintenance. It relies on two major unique selling points: a unified framework for predictive maintenance and an associated unified information system. There could be various options for Revenue Streams, which are investigated during the project to find the right balance between Value Chain management, Customers’ ability and willingness to pay, Competitors’ business models and the constraints and objectives of each member of the consortium. UPTIME approach deployment: beside the technical aspects, Customers will need assistance to transform its organization and adapt its maintenance strategy and all impacted business processes. This assistance will be charged per project.

Comment:

Currently SaaS is UPTIME main alternative to On-Premises solution.

Comment:

Currently still in discussion.

Comment:

The UPTIME Platform is deployed and validated against three industrial use cases:  (1) production and logistics systems in the aviation sector - FFT, (2) white goods production line - WHIRLPOOL  and (3) cold rolling for steel straps - MAILLIS.

The FFT use case deals with maintenance in highly complex transportation asset operations, some of which are mobile and subjected to diverse environments and a wide range of unpredictable environmental stress factors. Due to the critical nature of the deployment of these assets, the requirements are both technically and organisationally very high. The need has arisen to increase efficiency in maintenance execution as well as in reporting to the client, who in most of FFT projects is responsible for the logistics coordination of their assets.

The Whirlpool use case deals with the newly installed production line for clothes dryers, a complex automatic production line, which puts steel coils through a sequence of several steps of straightening, punching, seaming, flanging and screwing to eventually form the drum that holds and rotates the clothes in the dryer. The drum production equipment is very complex, highly automated and critical from many perspectives. It is critical to ensure the highest possible quality of the drum, which is the core component of the clothes dryer. At the same time, it is vital to keep the production equipment running efficiently and keep costs under control.

The Maillis use case deals with cold rolling mill for the production of steel strapping. MAILLIS‘s offer combines high-quality packaging materials and state?of?the?art technology, ensuring that metal producers enjoy reliable, durable, proven and high?speed strapping and wrapping technology at the optimal cost. MAILLIS uses cold rolling mills to produce rolling products with the smallest possible thickness tolerances and an excellent surface finish. The demand for changing over the milling rollers comes from either their regular wear or from an unexpected damage, which can occur due to either a defective raw material or an equipment malfunction. This usually happens every eight hours for the work rolls and every week for the backup rolls. After several regrinding, the diameter of the roll becomes so small that the rolls are no longer useful. It is expected to have a machine that reports its current health status along with the appropriate data analytics and metrics. UPTIME should also allow predictions about equipment‘s future health as well as recommendations for future actions and enable machines performing self-assessment, on which decision-making can be followed to advance equipment maintenance and facilitate the machine components and products life cycle.