Failure Modes Effects and Criticality Analysis (FMECA) is one of the analytical approaches to system design that is used to assess failure impacts. FMECA adds definition and estimation of the occurrence probability of each failure mode that can occur in a system and provides an evaluation of its consequences.
• a systematic and standard approach to identify failure modes and analyse risks in the design of a system creating a knowledge base
• allow for synergies among safety and cyber security by extending the concept of FMECA analysis also to intentional action (i.e. cybersecurity threats) and operator errors
• potential failure modes and risk analysis in the digital twin of a system
The UPTIME_FMECA module will provide maintenance recommendations along with appropriate logistics, production and quality-related advices by interacting with the other UPTIME Modules, such as UPTIME_DETECT & _PREDICT, _ANALYZE, and _DECIDE as well as providing a continuous improvement mechanism through its data-driven FMECA approach.
UPTIME_FMECA starts from the identification of the failure modes (i.e. how something can break down or fail) associated to each component of an equipment and analyse the impact of such failures on the whole system according to its physical and logical design. Failure occurrence probability is then estimated on the basis of simulations or historical records. The combination of these two elements gives feedback on the reliability of the chosen design identifying weak elements for which improvements are needed.
Target of this analysis is to reduce the consequences of critical failures. This is not only applicable to physical systems but also to processes as well. Effects to be evaluated go from local effect (i.e. related to the component itself and its functionality) to impacts on asset and production, people’s safety, environment and Company’s reputation, including potential domino effects.
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_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.
UPTIME_VISUALIZE provides configurable visualization to save time analysing data and getting insights, to support decision making and develop new solutions.
Zanardi, D., Barbieri, M., Uguccioni, G. (2018) IN: Zelm, M., Jaekel, F.W., Doumeingts, G. and Wollschlaeger, M. (ed.) Enterprise Interoperability: Smart Services and Business Impact of Enterprise Interoperability, First Edition. ISTE Ltd and John Wiley & Sons, Inc., pp. 285 – 290