The intelligent switch machine combines new, modern and robust sensor solutions with specialised adapted algorithms for evaluating the measurements. This supports the operator in monitoring the switch machine, the switch and its components, as well as in diagnosing and forecasting failure.

The central point is the measurement and display of the movement curve and the calculation of relevant parameters from it. The measurement is carried out with a universally applicable (retrofittable) sensor module. The system was optimized in such a way that it can be battery-operated/energy-autonomous.

In addition to the movement curves, the level of water in the housing, the oil level of hydraulic components and inspection-relevant variables such as the detector rod clearance can also be measured.

The aim is to replace or shorten inspections and to extend inspection intervals. This should result in an improvement of the LCC for the operator.

 The switch assembly monitoring module enables a safe operation of the switch by monitoring critical (switch) rail positions such as the distance between switch- and stock rail for the closed and open switch rail, as well as estimating the nearest flangeway.

If those values exceed a critical limit, the safe operation of the switch cannot be ensured anymore. Furthermore, the mechanical parts (switch- and stock rails) are subjected to plastic deformation which can negatively influence the rail position through laps and burrs. Additionally, they are subjected to wear and RCF which can cause rail degradation or even component failure due to breakouts, both increasing the risk for derailment.

Monitoring of these damage patterns and faults increases the operational safety of the switch and prolongs the lifetime of its components by setting corrective measures, before small defects can grow into break outs leading to component failure and thus to a replacement of the component (improved LCC, availability and safety).

The crossing area monitoring module enables diagnosis and prognosis of the wear state and based on that the remaining service life of critical crossing components e.g. the crossing nose (frog), the wing rails and the guard rails. Due to the discontinuity in the crossing area the mechanical parts in this region are subjected to high dynamic impact and creep forces which lead to a continuous degradation of the mechanical parts.

A target-oriented (tailored) monitoring solution facilitates condition based and prescriptive maintenance strategies by estimating the actual asset/component health state, predicting the remaining service-life and proposing corrective measures with sufficient lead time before the emerging defects lead to component failure and thus to traffic disruptions.

Furthermore, this module can be used for quality assessment of the passing wheels in order to detect worn or hollow worn wheels which increase dynamic loads and component degradation in the crossing panel.

Temperature has a significant influence on the track and its components. Therefore, temperature measurements are a non-negligible part of the monitoring and the maintenance recommendations derived from it.

The system can use newly installed or existing temperature sensors. These measurements are supplemented with data from weather services. This enables an algorithm to determine the temperature at different points on the rail.

On the one hand, this information can be used for planning tamping and welding work in the track and, on the other hand, for diagnosing and forecasting failures in the switch.

The vibrations induced by passing trains cause degradation and settlement in the ballast layer. Those effects lead to stiffness changes of the superstructure and increased movement of the sleepers.

With increased sleeper displacements, either caused by lower track stiffness or hanging sleepers (voids), the dynamic contact forces in the turnout increase significantly which further accelerates the degradation of the mechanical parts. By monitoring the sleeper displacement and rotation over time, track (ballast) degradation can be diagnosed and critical states can be predicted to propose the optimum period for track tamping with sufficient lead time.

In addition, the system can detect the operational parameters of the passing rolling stock material such as axle distances, train types, speed and passing route/direction which can be used for clustering and normalising the obtained data or capacity monitoring.