Visual Analytics Module

UPTIME_VISUALIZE deals with the definition, extension and implementation of data aggregation and visualization. The module provides individual, customizable and configurable visualisation (dashboard) to save time analysing data and getting insights, to support decision-making and develop new solutions.


UPTIME_VISUALIZE is highly dependent on the form in which the data processing components provide data to it, but conversely some requirements have also been defined based on “what we want to see” to facilitate in the optimum assessment capability for the end user.


•  Save time analyzing data and getting insights through role and user oriented simple/complex representation

•  Possibility to share info & insights with different domains

•  Reduced timespan to draw conclusions and actions from data representation

•  Location independent monitoring of status and reaction to events


UPTIME_VISUALIZE shows not only raw and pre-processed data as part of the basic functionality, but also GPS (logistics-relevant) and a representation of the asset with the goal to visualize health properties in the asset overview, to localize specific failures or indicate other local information in an intuitive manner. The possibilities to visualize semi-structured data, e.g. generating heat maps and tag clouds from frequency analysis of textual descriptions in reports (e.g. checklists) have resulted in the observation that acquisition (semi-automatic) and representation (in the data model) of such data would add significant value to facilitate qualitative assessments relevant to asset status.

UPTIME_VISUALIZE Dashboard Mock-Up of FFT Jigs Maintenance Based on Semi-Structure Asset Data

UPTIME Other Modules :

UPTIME_SENSE serves as modular data acquisition and manipulation components of the UPTIME Platform. The SENSE component captures data from a high variety of sources and cloud environments. It can connect to both analogue and digital data sources via numerous protocols, acquire data from these heterogeneous data sources, and integrate them towards a configurable data set. It is also capable of storing and intelligently handling and filtering the data acquired and can provide it to other subsequent UPTIME components in the form of sensor data streams for further analysis and processing. Moreover, it brings configurable diagnosis capabilities on the Edge, e.g. for real-time or off-the-grid applications.

UPTIME_DETECT aims to identify the topical state/condition of technical equipment by continuously observing sensor data streams. UPTIME_PREDICT includes abnormal behaviour of technical equipment and accordingly the classification of the condition state (simple example could be traffic light indication such as green, yellow red state). This is done by the possibility to orchestrate so‐called calculation flows based on diagnosis and prediction algorithms that are already built in the algorithmic framework of the tool or that are built on purpose by implementing a simple programming interface.

UPTIME FMECA, Failure Modes Effects and Criticality Analysis, aims to assess failure impacts of a system components. The FMECA component starts from the identification of the failure modes (i.e. how something can break down or fail) associated to each system’s component of an equipment and analyse the impact of such failures on the whole system according to its physical and logical design.

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 ANALYZE component is designed to handle data‐at‐rest which signify data collected from various sources and physically stored across different manufacturers’ information systems.

On the basis of (near) real-time predictions about future failures that lay outside the “normal states space”, DECIDE is enacted online in order to generate proactive action recommendations, i.e. recommendations about optimal (perfect or imperfect) maintenance actions and the optimal times of proactive action implementation. To do this, it estimates when the Expected Maintenance Loss will be minimized.