PIANiSM

Project results

PIANiSM's backbone lies in the developed architecture, a sophisticated framework comprising four layers for data acquisition, data pre-processing, model development and applications. Using this architecture, PIANiSM’s technical partners have each developed separate solutions for exploitation. Since the most important focus of the PIANiSM project is the predictive maintenance concept, it was of great importance to have diverse data sets that include different countries and sectors. For this reason, the project involved nine different use-case partners.

Exploitation

Thanks to PIANiSM, KoçSistem has supplemented its existing big data platform, Platform 360, with a PdM module that uses techniques such as data mining and model management to produce predictive maintenance reports, failure prediction and maintenance plans. A big automotive customer, that was already using Platform 360 before, has purchased the PdM module after the PIANiSM project.

Thanks to the updated Platform 360, the accuracy ratio of fault detection at this customer increased up to 84%, meaning the customer had information about these faults before they occurred and enabling the organisation to take the needed precautions in time.

Introducing a collaborative framework integrating data science, machine learning, and real-time processing within PIANiSM, Turkish SME ERSTE enhanced predictive maintenance in manufacturing. Leveraging the insights, ERSTE advanced further in other RD&I projects including ITEA project MACHINAIDE, and extended AutoML-powered solutions for streamlined equipment maintenance decisions.

Portuguese plastic film manufacturer Vizelpas supplied production and equipment data for the development of ISEP's predictive maintenance algorithms and enhanced Sistrade's monitoring tools, providing manufacturing expertise and feedback as required – all with a high degree of interaction and strong spirit of collaboration within the international consortium. This resulted in a set of algorithms and tools that could provide predictive insights and reports on equipment usage and maintenance to improve Vizelpas' equipment awareness capabilities. A strong impact has since been felt at an operational level as the company has successfully improved both the mean time between failure (MTBF) and the time taken to repair equipment. Increasing the uptime of equipment means a greater ability to meet the demands of customers, with a knock-on reduction in maintenance costs and a smaller parts inventory. From a human perspective, this also allowed Vizelpas to divert human resources away from manual equipment control and maintenance to higher value tasks.

Within PIANiSM, B3 Systems developed customised algorithms that not only predict equipment failures but also prescribe preventive measures, thereby reducing downtime and maintenance costs. This capability has enabled manufacturers to transition from traditional, reactive maintenance strategies to proactive, predictive approaches.

For instance, clients have reported a reduction in unplanned downtime by up to 40% and maintenance cost savings of 20-30%, significantly enhancing operational efficiency and productivity. The success of the PIANiSM project has positioned B3 Systems as a leader in the predictive maintenance domain.

Overall, PIANiSM has produced use-cases on ceramics, automotive, energy/ refineries, durable consumer goods, piping, plastic film/flexible packaging and data networks and has successfully demonstrated its system prototypes in an operational environment (TRL 7).