PROGnostics based Reliability Analysis for Maintenance Scheduling


Considering the increasing manufacturing plant size and complexity, unassisted maintenance management is becoming a losing strategy. Intense and global competition forces industries toward the maximal exploitation of their available resources, including equipment. However, the almost uninterrupted use of machine and robots degrades their performances and leads to an increased insurgence of breakages and failures. In turn this leads to higher costs due both to machine unavailability and expensive repair interventions.

The best solution to this problem would be to know at every given moment the real deterioration status of each piece of equipment. This knowledge would allow to select and schedule the best moment for maintenance, right before a component breaks or leads to unacceptable performance for the system/production chain of which it is part of.

Reliable information about the real component remaining lifetime can only be achieved by combining different techniques (trend analysis, components modelling, simulation, …) while the determination of the best maintenance schedule must rely on the correct assessment of the impact that each component has on the whole system as well as the compatibility with company’s production deadlines.

The PROGRAMS main objective is thus the optimal management of a factory maintenance activities, through the maximization of  operating life of production systems and the minimization of maintenance related LCC costs. In order to achieve these results, the proposed approach relies on a Smart Objects Technology (named Smart Control System) deployed at factory level and on the concept of cloud-enabled Central Application Layer, able to improve overall business effectiveness with respect to the following perspectives:

  • Increasing Availability (A) and then Overall Equipment Effectiveness (OEE) through increasing of MTBF.
  • Continuously monitoring the criticality of system components by performing/updating the FMECA analysis.
  • Providing alternative methods to estimate the condition and the Remaining Useful Life (RUL) of equipment components. (including physical-based models of the critical components and a data driven approach)
  • Determining an optimal strategies and policies for the maintenance activities of each equipment component.
  • Integrating the maintenance and production activities into a single schedule that optimizes the overall performances.
  • Providing robust and customizable data analysis services.
  • Allowing the company staff to actively participate to the maintenance management by an intra factory information service.

The PROGRAMS project aims at reaching its intended objective by developing the following novel technologies:

  1. An easy to install hardware system to collect process data and exploit them at machine level (WP2).
  2. Several alternative RUL computation methods (including a model-based prognostics method and a data driven approach) for the smart evaluation of equipment conditions (WP2-WP3)
  3. A support tool to speed up the FMECA deployment (WP4)
  4. A tool for reliability parameters estimation of components based on maintenance reports (WP4)
  5. A novel DSS tool for optimal maintenance strategy determination (WP4).
  6. A tool for merging maintenance and production schedules with integrated ERP support (WP4)
  7. A platform to share maintenance and machines related information between involved personnel (WP5).

The proposed approach will have an important impact especially on the SME sector, where the need for flexibility, production efficiency, and strategies optimization analysis, often meets resource limitation, localized knowledge gap, and lack of available tools.

The main benefits of the project outcomes are expected along the following lines:

  • Increased Availability and Overall Equipment Effectiveness

  • Continuous monitoring of system components criticality

  • Creation of physical-based models of the components which have a higher criticality level

  • Selection of optimal strategies for the maintenance activities

  • Integration of maintenance and production activities minimizing overall LCC

  • Provision of robust and customizable data analysis services

  • Development of an Intra Factory Information Service

The overall concept behind this project is based on 3 key layers:

  1. Physical Layer: predictive maintenance techniques relied on data monitoring

  2. Cloud Layer: allowing affordable time and space for elaboration techniques

  3. ICT Layer: ICT infrastructure and protocols which are the backbone for the other two layers

More information
Web resources:
Start date: 01-10-2017
End date: 30-09-2020
Total budget - Public funding: 5 995 272,00 Euro - 5 995 272,00 Euro
Call topic: Novel design and predictive maintenance technologies for increased operating life of production systems (FoF.2017.09)
Twitter: @programsEU
Cordis data

Original description

The main objectives of this project are to develop a model-based prognostics method integrating the FMECA and PRM approaches for the smart prediction of equipment condition, a novel MDSS tool for smart industries maintenance strategy determination and resource management integrating ERP support, and the introduction of an MSP tool to share information between involved personnel. The proposers' approach is able to improve overall business effectiveness with respect to the following perspectives:
• Increasing Availability and then Overall Equipment Effectiveness through increasing of MTBF, and reduction of MTTR and MDT.
• Continuously monitoring the criticality of system components by performing/updating the FMECA analysis at first implementation or whenever a variation in the system design or composition occurs.
• Building physical-based models of the components which have a higher criticality level or which status is difficult to monitor.
• Determining an optimal strategy for the maintenance activities.
• Creating a new schedule for the production activities that will optimize the overall system performance through a Smart Scheduling tool ensuring collaboration among the MDSS, the ERP and the RUL Estimation tool.
• Providing, in addition to traditional data acquisition and management functions in a machine condition monitoring system, robust and customizable data analysis services by a cloud-based platform.
• An Intra Factory Information Service will be developed to allow the company staff to be quickly informed of changes in the machine tool performances and to easily react to eventual production and maintenance activities rescheduling.

The production and maintenance schedule of complete production lines and entire plants will run with real-time flexibility in order to perform at the required level of efficiency, optimize resources and plan repair interventions.



Call topic


Update Date


Relevant items: View structured details below

Significant innovations and achievements Significance of the results for SMEs
Specific use case requirements Lessons learned

    PROGRAMS aims at developing a HW/SW suite of solutions capable of:

    1. Managing data relative to all maintenance strategies (including PdM) 
    2. Evaluating the cost associated to different maintenance strategies and policies
    3. Select and allocate the optimal maintenance Strategies (considering PdM) and Policies for each factory/machine assets (that minimise the overall cost)
    4. Allowing the seamless transfer of information from all factory levels
    5. Allowing easy PdM solutions deployment and exploitation
    6. Optimizing integration of PdM based maintenance with production activities
    7. Gathering and sharing maintenance information at all factory levels



    A survey among the customers of PROGRAMS industrial partners confirms that Predictive Maintenance practice will be effectively exploited ONLY IF it is:

    1. Affordable
    2. Confident and robust
    3. Easy to integrate
    4. Quick to deploy

    In addition  Predicitive maintenance solutions must be flexible enough to allow different objectives, like the avoidance of sudden failure or the limitation of performace degradation.


    Several challenges limit the succesfull application of Predictive Maintenance in factories:

    1. Lack of pre-existing maintenance data: Industry 4.0 is only slightly improving the deployment of tools for collecting data
    2. Difficult data synchronization: existing data is saved into tens of different formats and with different sampling frequencies
    3. Lack of sensors data relative to equipment fault status: equipment is never purposefully left to reach such a degraded status and, even then, faults happens only few times a year (so there is an high chance of never seeing faults during project duration).

Relevant items: View structured details below

Economic sustainability Product quality - Quality assurance Productivity
Skills, training, new job profiles Occupational safety and health Process reliability - dependability

    PROGRAMS solution will allow to gain a “10% increased in-service efficiency through reduced failure rates, downtime due to repair, unplanned plant or production system outages and extension of component life.”

    reduced failure rates:
    • analysis of real sensor data coupled with physically-based models allows to understand the real status of system components
    • optimal maintenance strategies allows to perform maintenance before components crash
    reduced downtime due to repair:
    • precise determination of components RUL will allow to replace/repair components before critical downtime
    • smart maintenance strategies allow to minimize maintenance impact on the production activities
    reduced unplanned plant/production system interruptions:
    • repair or replace of components before crash will lead to less unexpected components breakage
    • rationalized maintenance crews deployments will reduce the number of unavailable spare parts or maintenance teams.
    • shared  maintenance information will reduce time for reacting to unplanned outages.
    extension of component life
    • Smart RULE algorithms will allow to exploit a component up to its full useful life
    • tuning of processing parameters allows to use equipment for a longer time

    All these aspects will reduce the costs related to maintenance activities, thus increasing the sustainability of the production process.


      PROGRAMS solutions will allow a more widespread adoption of predictive maintenance as a result of the demonstration of more accurate, secure and trustworthy techniques at component, machine and system level. In fact one of the biggest obstacles is getting people to change long-held maintenance practices. PROGRAMS will:

      • Demonstrate that, when correctly applied, the LCC of machines and components actually decreases when using PdM based technology, while the machine availability increases.
      • Provide an easy to install solution able to detect the deterioration of performance (at component, machine and system level) and manage maintenance accordingly.
      • Implement SotA security layers, showing that the security of maintenance data transmission methods is on par with widespread and accepted communication protocols, like the ones used for e-commerce.

      Precise determination of the RUL of components will allow to replace them before their status degrades production equipment performances beyond unacceptable levels.


      Reducing failure rates and production equipment unavailability will improve factory productivity.


      PROGRAMS solution will increase accident mitigation capability”

      • Component failures will be reduced by optimal maintenance strategies and analysis of real sensor data coupled with physically-based models .
      • Maintenance efficiency will be increased by precisely pinpointing the components whose deterioration is or will impairing the system performance.
      • PROGRAMS will allow to identify design problems or faulty components batches by comparing initial FMECA analysis with updated reliability information.
      • A plant wide network will constantly update employees on maintenance activities and components degraded performances.
      • The automatic tuning of control parameters will mitigate the impact of a damaged/worn components until maintenance can restore them to their optimal conditions.

      Predictive maintenance requires different skills and thus new professional figures will have to to be trained:

      • Production equipment operators
      • Maintenance operators
      • Data scientists
      • Maintenance managers
      • Software developers


Relevant items: View structured details below

Integration with legacy systems Platform level interoperability
AAA - Access, Authorisation and Authentication User Acccess and Rights Management

Relevant items: View structured details below


    More than 230 existing Standards have been collected and analysed by the PROGRAMS consortium. As a result, 10 of them have been considered extremely important (labelled as ‘Mandatory”) to foster exploitability of project results. These identified Standards refers mainly to ICT-related topics such as cyber security, data communication/exchange protocols, data mining and control SW.