Non-relational database (NoSQL) Comments 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
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.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
UPTIME will reframe predictive maintenance strategy in a systematic and unified way with the aim to fully exploit the advancements in ICT and maintenance management by examining the potential of big data in an e-maintenance infrastructure taking into account the Gartner’s four levels of data analytics maturity and the
proactive computing principles.
UPTIME 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:
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.
The data visualisation in UPTIME is performed by the UPTIME_VISUALIZE (SeaBAR prototype) component:
UPTIME will enable manufacturing companies to reach Gartner's four levels of data analytics maturity for optimised decision making - each one building on the previous one: Monitor, Diagnose and Control, Manage, Optimize - aims to optimise in-service efficiency and contribute to increased accident mitigation capability by avoiding crucial breakdowns with significant consequences. UPTIME Components UPTIME_DETECT & UPTIME_PREDICT and UPTIME_ANALYZE aim to enhance the methodology framework for data processing and analytics. The key role for the UPTIME_DETECT and UPTIME_PREDICT components are data scientists who are in charge of developing, testing and deploying algorithmic calculations on data streams. In this way, the component is able to to identify the current condition of technical equipment and to give predictions. On the other hand, the UPTIME_ANALYZE is a data analytics engine driven by the need to leverage manufacturers’ legacy data and operational data related to maintenance, and to extract and correlate relevant knowledge.
The 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.
The "Persistence" layer in the UPTIME conceptual architecture includes a Database Abstraction Layer (DAL) and houses of relational database engine as well as a NoSQL database, where all information needed by the "User Interaction" and "Real-Time Procesing and Batch Processing" layers (refer to UPTIME conceptual architecture) is stored and retrieved. For the raw sensor data itself, this data storage concept is enhanced by a database for time-series data to ensure efficient and reliable storage, while visualization functionalities will use an indexing database to facilitate the exposure of analytics. In these databases, all information needed by the other three layers is stored and retrieved. The UPTIME solution aims to provide data harmonization in terms of manipulating streaming data coming from the sensors. Based upon these needs a time series database is needed and in the context of UPTIME three instances of influxDB (one instance per business case) are installed. Along with the influxDB instances, a common MySQL database that will handle the operations of the UPTIME system is created.
UPTIME has a common MySQL database that will handle the operations of the UPTIME system.
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.
In UPTIME, two data processing solutions are considered. (1) Batch processing of data at rest, through massively paralle processing, (2) real-time processing of data in motion, real time data from heterogeneous sources are processsed as a continuous "stream" of events (produced by some outside system or systems), and that data processing occurs so fast that all decisions are made without stopping the data stream and storing the information first.
UPTIME main functionalities are structured in three main modules, namely: edge, cloud and GUI modules.
4 main components in the cloudbased infastructure of the UPTIME platform include:
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:
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.
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.
To ease integration of all UPTIME components, the main programming language used by the components and the integrated platform is Java.
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.
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.
- 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.
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: