Forecast rail network performance with renewed POSS platform

The Power of Algorithms

POSS, Strukton’s monitoring system, uses big data, IoT and machine learning technology to provide rail asset owners with 24/7 insight into the performance of critical assets. Smart algorithms analyse and interpret the collected data and report on the root causes of failure modes. This enables asset owners, managers and contractors to intervene at the right time and at the right place.

One of the many heard challenges faced by railway asset managers is the obligation to achieve strict network performance objectives while the infrastructure is subjected to higher loads due to a higher frequency of train services. The ascending number of passenger kilometres implies increased degeneration of network performance, while less time is available for inspection and maintenance activities. This calls for a targeted maintenance approach.

As a smart rail maintenance specialist, Strukton Rail strives for continuous improvement of maintenance processes, resulting in optimised network availability. A key component of its approach is the transfer of condition data into predictive values and actionable information.

From Time-based Maintenance to Predictive Maintenance

The POSS Online platform R7 improves the insight into the condition of assets, enabling asset managers to better manage and control maintenance processes. POSS Online R7 converts asset condition data into information that adds predictive value to maintenance strategies. It simply facilitates to look back, look at and to forecast asset-performance by identifying improvement potential. It does so through advanced analysing tools and by deploying smart algorithms coupled to domain knowledge in order to automate the root cause analysis and provide actionable information. This supports the transition from a time-based maintenance regime to a predictive maintenance regime.

Virtual maintenance assistant

The new POSS Online R7 platform acts as a virtual maintenance assistant supporting the development of maintenance strategies. The Strukton Control Center evaluates actionable information to assist rail asset managers during their transition capturing the potential of an predictive maintenance strategy.

Obviously the POSS Online R7 platform facilitates the use of smartphones, laptops and other devices to obtain and exchange performance information. The platform facilitates multiple users and applications, and connects to other existing and upcoming platforms. The open architecture makes the platform and analysis tooling future-proof for integration with e.g. IoT sensing technology. This provides uniformity and standardisation in the journey to the next step(s) in predictive maintenance to achieve optimised availability and safety against lower TCO.