This article first appeared in the Railway-News magazine, Issue 2 2024.
Railway wheelset inspections are vital to ensuring the safety, efficiency and reliability of rail transportation systems.
During operation, wheelsets encounter a high degree of stress, which can lead to wheel wear, fatigue cracks and misalignments that can compromise the integrity of the wheelset and cause knock-on damage to tracks and other rail infrastructure. Detecting these issues as early as possible is key to preventing accidents, derailments and other safety hazards.
Nondestructive testing (NDT) of rail wheelsets can be a time-consuming and laborious task when approached via manual conventional ultrasonic testing (UT) or phased array ultrasonic testing (PAUT) methods. This is primarily due to key components such as the axle, rim and tread being difficult to access, meaning that wheelset disassembly is sometimes necessary to complete inspections to a satisfactory standard. Following disassembly from the carriage, it can still be challenging to access key components for inspection, requiring multiple probes to be set up at various angles to gain an appropriate level of coverage. This lengthy and cumbersome workflow can have significant financial implications, from increased railcar downtime to labour costs. Manual UT/PAUT inspection results are highly human-dependent, and as such, prone to operator errors. Furthermore, manual inspection data often lacks traceability since the data is volatile, and not always stored on a local system.
The PASAWIS (Phased Array Semi-Automated Wheelset Inspection System) is a next-generation, all-in-one solution that makes wheelset inspections substantially more efficient, faster and more reliable. Developed by Evident, in collaboration with Fraunhofer IZFP and RailMaint, this powerful testing platform meets the requirements of the VPI-EMG09 regulation for the maintenance of freight cars through the implementation of NDT.
Use the form opposite to get in touch with Evident directly to discuss any requirements you might have.