13, December 2019

ESA Open Invitation to Tender AO10127
Open Date: 11/12/2019
Closing Date: 31/01/2020 13:00:00

Status: ISSUED
Reference Nr.: 19.1AA.05
Prog. Ref.: IAP Phase 3
Budget Ref.: E/0520-01D – IAP Phase 3
Tender Type: C
Price Range: 50-100 KEURO
Products: Non Space Procurement/Services
Technology Domains: Others
Establishment: ESTEC
Directorate: Directorate Telecom & Integrated Applica
Department: Downstream Business Applications Dept.
Division: Study and Project Management Office
Contract Officer: Fiumi, Federica
Industrial Policy Measure: N/A – Not apply
Last Update Date: 11/12/2019
Update Reason: Tender issue

Predictive maintenance is gaining importance in various sectors, including government, aerospace defence, energy utilities, manufacturing, healthcare, and transportation logistics, due to the advantages it offers over routine or time-based preventive maintenance. The major growth drivers for the predictive maintenance industry include growing demand for big data and internet of things, and increasing concern of organizations to reduce the asset operation and maintenance costs. Predictive maintenance techniques determine the condition of in-service equipment to predict when maintenance should be performed, thereby minimizing disruption of normal system operations. This leads to reduced down times and therefore substantial cost savings and higher system reliability. The ultimate goal of the approach is to perform maintenance at a scheduled point in time when the maintenance activity is most cost-effective and before the equipment loses performance within a threshold. With the knowledge of when equipment needs to be serviced and whatneeds to be done, maintenance work can be planned optimally with the right resources in place. Depending on the type of applicationand equipment additional advantages can materialise like the reduction of accidents associated with equipment failure and increasedequipment lifetimes.Predictive maintenance solutions rely on a number of elements:1.Automated condition monitoring must be installed on the target machines/assets or infrastructure. 2.Embedded processing to handle first analysis of raw data, turning it into useful information that can be shared with supervising systems. In addition, the amount of data that needs to be communicated is vastly reduced. 3.Communication of information to local and remote supervising systems. This communication must be done securely and efficiently.4.Predictive model for the equipment failure mode(s). Machine learning techniques can be used to create a more refine predictive maintenance model.5.Transfer of the predictive maintenance model results to a computerized maintenance management system (CMMS) so that the equipment condition data is sent to the right equipment object in the CMMS system in order to trigger maintenance planning, work order execution, and reporting (logistics). This kick-start initiative therefore encompasses services that support parts of or predictive maintenance solution as a whole as well as supporting the transition from other maintenance approaches.

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