UPTIME_VISUALIZE
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.
VISUALISATION FROM HETEROGENEOUS DATA SOURCES
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.
VISUAL ANALYTICS
The UPTIME Platform consists of six main components, addressing various phases of the unified predictive maintenance approach. In our previous newsletters, we introduced UPTIME_SENSE for data acquisition and manipulation, UPTIME_DETECT & _PREDICT for stream data analytics, UPTIME_DECIDE for maintenance decision-making and action planning, UPTIME_ANALYZE for batch data analytics and UPTIME_FMECA for risk assessment. In this edition, we present the last component UPTIME_VISUALIZE for visual analytics.
The UPTIME Platform is a unified platform with end-to-end integration and communication among its functional parts. UPTIME_VISUALIZE is a Human-Machine Interface, which visualizes data in the appropriate, customer-oriented way and allows the user to monitor and interact with the data in real time as well as in an offline mode for detailed batch data analysis.
UPTIME_VISUALIZE mainly deals with the definition, extension and implementation of data aggregation and visualization. It provides individual, customizable and configurable visualisation (dashboard) to save time analysing data and getting insights, to support decision-making and develop new solutions.
Implementation of UPTIME_VISUALIZE in the MAILLIS Business Case
Currently, machine maintenance and monitoring in the MAILLIS business case is done by visual inspection and based on the engineer’s judgement.
One of the main aims of UPTIME_VISUALIZE data visualization is to provide optimum maintenance times by inspecting the status of the rollers and bearings through sensors attached to the rollers. This is crucial since, during operation, there is no way to know the conditions inside the milling stations; acoustic or visual cues of abnormal operation usually come too late after a significant amount of damage has been done to the roller (e.g. jamming, roller breaks) or in the bearings.
For the MAILLIS business case, data about velocity and acceleration at the roller are collected and visualized, for example, as shown in following figure. In addition, a Shock Finder Indicator is implemented to provide reliable data even in low speed operation.
The maintenance staff can easily monitor data coming from different sensors at different places of the rollers and the motor of the milling station. The time span visualized is the same in all graphs, regardless of real time or batch data. This allows for a quick correlation of sensor data streams and the identification of areas that can have a significant impact. Decisions on an early inspection outside of the usual maintenance cycle times can be made right away. These decision can of course be supported by the other UPTIME Platform components already described in previous newsletters.
If you are interested in potentially deploying UPTIME_VISUALIZE in your context, please feel free to contact us!
Dr. Bernd Bredehorst Senior Project Manager Head of Department Knowledge & Innovation Pumacy Technologies AG
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.