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.

Location

This is a set of Specific Objectives and Research & Innovation Objectives that is subject to a consultation in preparation of the Made In Europe Partnership.  For more guidance about the consultation, please see www.effra.eu/made-in-europe-state-play.

    Comment:

    UPTIME provides a methodology (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 is 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 utilizes 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 also incorporates 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:

      Taking advantage of the big data generated from the large amount of sensors within the industrial IoT requires the development of event monitoring and data processing systems that are able to handle real-time data in complex, dynamic environments. UPTIME architecture is developed based on a flexible and modular system. UPTIME solution makes the use of the availability of big data for the real-time processing and store them in an efficient and scalable way with the aim to predict undesirable situations and enable to decide and act ahead of time.

    Comment:

    Modern Manufacturing activities have become more and more efficient, either in terms of Non-Quality reduction, Costs optimization and performance efficiency. On the other hand, products become every day more complex, and unbreakable: no need to replace the product, but to maintain it. Manufacturing activities are producing lots of waste, recycling not being so far addressed by Industrial Companies and the Circular Economy principles need to be further promoted and deployed. The UPTIME solution is an external data analysis system, able to capture Means failures and defects (through the installation of captors and sensors on some key equipment and systems inside the Machine). UPTIME solution is able to analyse the erosion of critical components and systems, and establish an automated Predictive Maintenance Plan, adapted to real-time failures measurements – not only Return-On-Experience. This Analysis solution provides a large flexibility by adapting the maintenance plan to real events. The solution can be applied to any kind of products, from the moment that Machines or Products get embedded sensors.

      Comment:

      There are mainly 2 positive impacts, which UPTIME contributes to the environment:

      • Reduction of scrap: UPTIME will reduce non-quality and then avoid wasted raw materials and wasted energy
      • Extension of equipment lifetime: since UPTIME will support to decide preventive action before equipment failure, it is expected that major breakage could be avoided.
      Comment:

      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. 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.

    Comment:

    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 endeavours to promote adoption of predictive maintenance in manufacturing and eventually open up the perspective for new business models’ adoption through its concrete dissemination and exploitation activities (i.e. dedicated demonstrations to potential future users, demonstrations in industry fairs, clear and concise adoption methodology).

    UPTIME will give an impact on technology and innovation capacity in manufacturing industries through:

    • Adoption of IoT technologies: companies will learn how to master IoT technologies and what possibilities they offer
    • Conversely, IoT technology providers will benefit and learn from this challenging application case: high density of connected sensors, harsh environment, demanding operational performance
    • Digital transformation of industrial firms: thanks to UPTIME, companies will initiate (or reinforce) a transformation of their capabilities and business models towards a concrete application of Factory 4.0. The Digital approach becomes a global perspective.
      Comment:

      UPTIME data acquisition component, UPTIME_SENSE will ensure an uptake of the shared use of sensor data, significantly reducing data overhead and acquisition efforts, while its use case specific implementation will ensure that the project use cases will be supported in the necessary way. Beyond the technological advance of sensor interfacing and intelligent data usage the UPTIME_SENSE component will further redefines the state of the art amongst others in regards to open data acquisition and manipulation for predictive maintenance including establishing of trust chains over multiple stakeholders in the data chain to ensure high data/information integrity and trust.

      Furthermore, the UPTIME integrated 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.

      Comment:

      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.

      UPTIME will be able to provide maintenance recommendations along with appropriate logistics, production and quality-related ones (e.g. for changing the production plan and/ or avoid malfunctions leading to poor quality) by interacting with the data and information existing in the manufacturing company’s systems.

    Comment:

    Unlike robotization, predictive maintenance will not lead to the suppression of human tasks but to an optimization of their organization. A second impact will be a possible reduction of workplace injuries thanks to:

    • Better activity planning leading to less high pressure situations and more smoothly planned activities
    • Anticipation of machine/tooling break which could lead to dangerous situations
      Comment:

      UPTIME predictive maintenance platform 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.

      UPTIME integrated platform will be able to 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 e-Platform is expected to increase in-service efficiency by at least 10% in the 3 pilot cases.

      Comment:

      Maintenance management decisions at the operational level are usually taken by human experts on the basis of information derived from the equipment itself and/or from appropriate information systems. 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 solution is overall novelty, in which each tool addressing an e-maintenance service is also novel on its own and is distinguished from its competitors. UPTIME provides a unified predictive maintenance framework and an associated unified information system in order to enable the predictive maintenance strategy 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 the four levels of data analytics maturity. The unification of the novel e-maintenance services and tools in the context of the proposed framework will lead to overcoming of the existing commercial software and research prototypes limitations and will conclude in a novel predictive maintenance solution covering the whole prognostic lifecycle.

      Change management, innovation monitoring and coaching are key aspects for a widespread deployment of UPTIME. It is critical to take care of the changes of the relations of the actors. Technology impacts these immaterial structures and need to be anticipated as early as possible.The sharing of data, possibly for legal and property issues may be an obstacle. Practically speaking, the heterogeneity of the data models and of shared/used standards is a clear obstacle. Data silos and lack of shared reference data libraries to enable interoperability of applications and proper interpretation of retrieved information or reusable knowledge is also a barrier. In this manner, the use of existing or emerging frameworks such as RAMI 4.0 (Referenzarchitekturmodell Industrie 4.0) or IIRA (Industrial Internet Reference Architecture) it is likewise important to reduce obstacles as far as possible related to the implementation and interoperability of non-standardized frameworks.

      Comment:

      The UPTIME integrated 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. In light of the “Digitising the European Industry” and the Industry 4.0 initiatives, UPTIME endeavours to promote adoption of predictive maintenance in manufacturing and eventually open up the perspective for new business models’ adoption through its concrete dissemination and exploitation activities, i.e. UPTIME undertakes a targeted campaign in predictive
      maintenance strategy to attract new members, in particular manufacturing companies, consulting firms and
      SMEs to the UPTIME community/ecosystems to use its e-maintenance services. UPTIME will also set up a dissemination network to provide regular information and updates about activities in the UPTIME ecosystems (https://www.uptime-h2020.eu/index.php/partner-programme/).

    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: 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.
    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.

    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:

      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:

      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:

      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:

      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 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:

    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:

    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 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:

        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 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:

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

        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 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:

        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:

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

      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:

        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:

      UPTIME will provide a unified predictive maintenance framework and an associated unified information system in order to enable the predictive meintenance strategy implementation in manufacturing firms with the aim to maximize the expected utility and to exploit  the full potential of predictive management, sensor generated big data processing, e-maintenance, proactive computing and the four levels of data analytics maturity. The unification of the novel e-maintenance services and tools in the context of the proposed framework will lead to overcoming of the existing commercial software and research prototypes limitations and will conclude in a novel predictive maintenance solution covering the whole prognostic lifecycle.

      Some limitiations of existing e-maintenance commercial software and research prototypes that will be addressed by UPTIME Platform, for example:

      • The majority of such prototypes and commercial systems focus on product maintenance, i.e. on the service stage of the PLM (e.g. warranty failures) and not on industrial maintenance, i.e. on the manufacturing stage of the PLM.
      • The developed systems are mainly based upon physical, domain-specific models that are not easily extensible for other equipment or for other industries.
      • They rarely exploit big data processing infrastructures for real-time, sensor data, since they usually use batches of data, while the level of data analytics maturity is usually low.
      • They do not allow the embodiment of domain knowledge in order to be used by appropriate methods and to be coupled with the real-time data.
      • Each one of them focuses on a specific aspect of predictive maintenance (e.g. condition monitoring, prognostics, maintenance spare parts) instead of having a unified approach for covering all the phases and related aspects in terms of industrial operations management.

       

    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
    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 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:

        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 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:

      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.

UNIFIED PREDICTIVE MAINTENANCE SYSTEM
Latest news
Indah Lengkong
26/06/20 - 11:22
UPTIME Newsletter - June 2020

Welcome to the 6th edition of the UPTIME Newsletter!

The UPTIME project has entered the evaluation phase. The prototype of the UPTIME Platform has been integrated into the three industrial business cases in its final configuration. In this edition, MAILLIS shares with us their first-hand experience and some lessons learned from the recent implementation of the UPTIME Platform in the MAILLIS Business Case, which deals with predictive maintenance in a cold rolling mill for the production of steel strapping.

In the previous newsletters, we introduced five of the UPTIME Platform’s main components. In this issue, we’ll present the last component prototype, UPTIME_VISUALIZE, which provides individual, customizable and configurable visualisation of data in a customer-oriented way. Its implementation in the MAILLIS Business Case is used as an example of how it works.

We have also been organising a series of webinars showcasing the main features of the UPTIME Platform including live demonstrations of their implementation in our industrial use cases. The first webinar on the implementation in the White Goods case was held on 19 March 2020. In case you missed it, you can find the webinar recording here.

The second webinar, “UPTIME Predictive Maintenance: Lessons Learned and Best Practices in the Steel Industry“, will take place on Thursday, 09 July 2020, 11:00 – 12:30 CEST. We hope you can join our webinar and look forward to interacting with you and getting your valuable feedback. To register and get more detailed information about the webinar, please click here.  We hope you enjoy our newsletter!

A full newsletter is available: https://www.uptime-h2020.eu/index.php/2020/06/26/uptime-6th-newsletter-edition-06-2020/

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Indah Lengkong
18/06/20 - 07:34
UPTIME 2nd Live Webinar: Predictive Maintenance - Lessons Learned & Best Practices in Steel Industry - 9 July 2020

REGISTRATION

Date: Thu, 09 July 2020

Time: 11:00 - 12:30 CEST

 

Are all of the maintenance activities performed at a certain period of time necessary or can maintenance intervals be expanded in order to reduce the economic impact?

The UPTIME 2nd Webinar will address the key questions above and illustrate benefits of predictive maintenance by a concrete implementation in the Steel Industry. MAILLIS business case dealing with cold rolling mill for the production of steel strapping will be presented and its implementation in the UPTIME Platform will be demonstrated.

It is of outmost importance for manufacturing to have their machine or a piece of equipment that can tell its current health status and the degree to which that status deviates from normal or healthy along with predictions about its future health state, as well as actions recommendations. UPTIME will allow machines to perform self-assessment, on the basis of which decision making can be significantly enabled, to anticipate planned intervention on machines, to reduce unexpected breakdowns and delay other interventions, thus save money and improve safety.

The webinar is free of charge, dedicated to people who want to learn and see a concrete implementation of the UPTIME Predictive Maintenance Platform in a real business case. It is interactive, where you have the opportunity to ask questions to the experts panel and we are happy to receive your feedback.

If you have any questions or comments, please contact us.

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19/10/2020 08:20:01 - Uptime-H2020 -@uptimeH2020
Some practical implementation in the industrial cases will be presented tomorow at the ForeSee Webinar #2, by @ProphesyProject & @z_bre4k partners Haije Zijlstra & Daniël Caljouw, Philips,@programsEU partners, Michele Surico @Fidia_Iberica & Ignacio Martínez de la Pera, Aurrenak. [Go to tweet]

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